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Multi-Satellite Data Integration for Operational Ship Detection, Identification and Tracking (DIOS)

Progress Report 2

Sherry Warren Des Power Thomas Puestow Igor Zakharov Mark Kapfer Mark Howell Michael Lynch Pamela Burke Robert Hewitt C-Core Prepared by: C-CORE Capt Robert A. Bartlett Building 1 Morrissey Rd St. John’s, Newfoundland-and-Labrador Canada, A1B 3X5 Contractor's document number: R-19-011-1443 PSPC Contract Number: W7714-186608/001/sv Technical Authority: Daniel Lavigne, Defence Scientist Contractor's date of publication: May 2019

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January 2020DRDC-RDDC-2019-C210Contract Report

Defence Research and Development Canada

Template in use: EO Publishing App for CR-EL Eng 2019-01-03-v1.dotm

© C-CORE, 2019

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IMPORTANT INFORMATIVE STATEMENTS

This document was reviewed for Controlled Goods by Defence Research and Development Canada using the Schedule to the Defence Production Act.

Disclaimer: This document is not published by the Editorial Office of Defence Research and Development Canada, an agency of the Department of National Defence of Canada but is to be catalogued in the Canadian Defence Information System (CANDIS), the national repository for Defence S&T documents. Her Majesty the Queen in Right of Canada (Department of National Defence) makes no representations or warranties, expressed or implied, of any kind whatsoever, and assumes no liability for the accuracy, reliability, completeness, currency or usefulness of any information, product, process or material included in this document. Nothing in this document should be interpreted as an endorsement for the specific use of any tool, technique or process examined in it. Any reliance on, or use of, any information, product, process or material included in this document is at the sole risk of the person so using it or relying on it. Canada does not assume any liability in respect of any damages or losses arising out of or in connection with the use of, or reliance on, any information, product, process or material included in this document.

Multi-Satellite Data Integration for Operational Ship Detection,

Identification and Tracking (DIOS) Progress Report 2

C-CORE Document Number R-19-011-1443

Prepared for: Defence Research and Development Canada (DRDC)

Revision 1.0 May, 2019

This page is intentionally left blank

Multi-Satellite Data Integration for Operational Ship Detection, Identification and Tracking (DIOS), Progress Report 2 PREPARED FOR Defence Research and Development Canada (DRDC) DOC ID R-19-011-1443 REVISION 1.0 DATE May, 2019

The correct citation for this document is: C-CORE. 2019. “Multi-Satellite Data Integration for Operational Ship Detection, Identification and Tracking (DIOS).” Proposal R-19-011-1443, Revision 1.0. Project Team Sherry Warren (Project Manager) Des Power Thomas Puestow Igor Zakharov Mark Kapfer Mark Howell Michael Lynch Pamela Burke Robert Hewitt


Revision History

Version Name Date Of Changes Comments

1.0 Sherry Warren Igor Zakharov 05/22/19 Submitted to Client

Distribution List

Company Name Number Of Copies

Defence Research and Development Canada (DRDC)

Daniel Lavigne Paris Vachon Mike Sale

1 electronic


Table of Contents

1 INTRODUCTION .................................................................................................................................... 1

1.1 Background and Objectives ........................................................................................................ 1

2 PROJECT MANAGEMENT (WP 1000) .................................................................................................... 3

2.1 Methodology ............................................................................................................................... 3 2.2 Work Package Descriptions (WBS) .............................................................................................. 4 2.3 Schedule and Budget .................................................................................................................. 5 2.4 Deliverables ................................................................................................................................. 5

3 DATA COLLECTION AND VALIDATION (WP 3000) ................................................................................ 8

3.1 Plan for Field Data Collections and Ground Truthing ................................................................. 8 3.2 Cloud Cover Assessments ......................................................................................................... 10

3.2.1 Sentinel-2 ....................................................................................................................... 10 3.2.2 MODIS ............................................................................................................................ 12 3.2.3 Meteosat ....................................................................................................................... 13

4 ALGORITHM DEVELOPMENT FOR SHIP CLASSIFICATION (WP 4000) ................................................. 16

4.1 Ship Detection in Medium Resolution EO/IR data .................................................................... 16 4.1.1 Sentinel-2 Imagery ......................................................................................................... 16

4.2 Ship and Cloud Discrimination .................................................................................................. 20 4.2.1 Feature Selection ........................................................................................................... 21 4.2.2 Collecting Training Data ................................................................................................. 21 4.2.3 Classification Algorithms ............................................................................................... 21 4.2.4 Landsat 8 Imagery.......................................................................................................... 24

4.3 VHR Data: Testing on WorldView-2 Data .................................................................................. 25 4.4 Low Resolution EO/IR Data ....................................................................................................... 27

4.4.1 Day-Night Band of Suomi NPP satellite ......................................................................... 27 4.5 Target Detection Using Altimetry Data ..................................................................................... 27

5 MULTI-SENSOR TRACKING (WP 5000) ............................................................................................... 32

6 FUTURE WORK ................................................................................................................................... 34

7 DIOS PROJECT RESULTS RELEVANT TO DND AND CAF ....................................................................... 35

8 REFERENCES ....................................................................................................................................... 36


List of Tables

Table 1. Work Packages ................................................................................................................................ 5 Table 2. List of Project Management Deliverables ....................................................................................... 6 Table 3. List of Work Package Deliverables .................................................................................................. 6 Table 4. Vessel types extracted from satellite EO images. ......................................................................... 32

List of Figures

Figure 1. Flowchart of multi-satellite data integration for ship detection, classification, identification and tracking. ................................................................................................................................................ 4

Figure 2. Updated Project Schedule ............................................................................................................. 5 Figure 3. Port of St. John’s, NL Map of active ships in Port. (Taken from Marine Traffic website) .............. 9 Figure 4. Port of Halifax, Nova Scotia Website ............................................................................................. 9 Figure 5. Cloud-free Sentinel-2A image of Halifax, Nova Scotia captured on July 23, 2017 ...................... 11 Figure 6. Cloud-free Sentinel-2A image of St. John’s, NL captured on July 26, 2018 ................................. 12 Figure 7. MODIS Cloud Cover Frequency for April – July from 2000-2014 for two study areas. ............... 13 Figure 8. Metosat Cloud Cover Percentages for April – May 2016 ............................................................. 14 Figure 9. Metosat Cloud Cover Percentages for April – May 2017 ............................................................. 14 Figure 10. Metosat Cloud Cover Percentages for April – May 2018 ........................................................... 15 Figure 11. Flowchart of ship detection algorithm using Sentinel-2 data. ................................................... 16 Figure 12. Image of Band 8 with ships in the ocean (left) and the corresponding histogram (right). ........ 17 Figure 13. Detection of ships near the Strait of Gibraltar in Sentinel-2 image (2019-03-25) using CA CFAR

technique. The width of image fragment is 110km. .......................................................................... 18 Figure 14. Examples of ships detected in a Sentinel-2 image. The length of the ship on the right is

approximately 400 meters and the other ships are comparable sizes. ............................................. 18 Figure 15. Sentinel-2 RGB Image (2019-03-23) and cloud mask created using Band 10 (1375nm). .......... 19 Figure 16. Sentinel-2 RGB Image (top) and Band 11 (1610nm) (bottom) with ships and small clouds. .... 20 Figure 17. Classification accuracies (ship/non-ship classification and misclassification rates) for all

classifiers. ........................................................................................................................................... 23 Figure 18. Map of detected targets in Sentinel-2 image after discrimination using QDA: green triangles

represent ships and blue circles indicate clouds. ............................................................................... 24 Figure 19. Detection in Landsat-8 imagery (2019-04-13): cloud mask generated using modified ACCA

algorithm (on the left) and ship (green triangles) and cloud (blue circles) classification results (on the right). The image width is about 190km. ........................................................................................... 25

Figure 20. Detection results for WorldView-2 image (on the left) over Sydney Harbour achieved using the adaptive thresholding (on the right) and saliency-based approach (in the middle). Image width is 1km. .................................................................................................................................................... 26

Figure 21. Ship (bright point) near the Labrador coast in DNB Suomi-NPP VIIRS on August 18, 2018 (image courtesy NASA and NOAA). ................................................................................................................ 27

Figure 22. Example of signatures of an iceberg (left) and a ship (Right) in Ku- and C- bands. ................... 28


Figure 23. Ground tracks of Jason-3 altimeter for one repeat cycle (10 days). A red rectangular indicates an area used for target detection. ..................................................................................................... 29

Figure 24. Targets detected with Jason-2 (black triangles) and Jason-3 (blue triangles) altimeters in August 2018. ................................................................................................................................................... 30

Figure 25. Targets classified as ships (stars) and icebergs (triangles) in August 2018. ............................... 31 Figure 26. L/W ratios extracted from EO images (left) and SAR images (right) as a function of vessel type

............................................................................................................................................................ 33


C-CORE R-19-011-1443 to DRDC 1

1 Introduction This document is a deliverable under Defence Industrial Research Program (DIRP) Contract No. W7714-186608/001/sv. It provides an update on the technical and management progress for Multi-Satellite Data Integration for Operational Ship detection, identification and tracking (DIOS) since the Progress Review Meeting held on January 31, 2019.

1.1 Background and Objectives DIOS addresses the Maritime Surveillance Strategic Objective 5 (SO5) by using electro-optical and infrared (EO/IR) sensors to compliment the RADARSAT Constellation Mission (RCM) mission for ship detection, identification, classification and tracking. This project will investigate new ways of monitoring shipping traffic and icebergs using multiple sensors, including RADARSAT-2 (RS-2) and TerraSAR-X (TSX). It also builds on previous DIRPs and the current DIRP titled “Tactical Detection of Icebergs and Ships in Sea Ice for Polar Epsilon 2 (TACTICS-PE2)”. DIOS will build on what was learned from the previous TACTICS/ShipTac project to increase ship detection and discrimination based on a multi-satellite data integration approach using C-Band and X-Band, EO/IR, and radar altimetry.

C-CORE will investigate and develop a multi-satellite data classifier that determines if an unknown target is a ship or non-ship. C-CORE will deliver a Technical Note (TN) that describes the classifier algorithm that can be exploited by Polar Epsilon 2 (PE2) software developers1. Further to the RCM, DIOS will also investigate TSX X-Band capabilities, EO/IR and radar altimetry and analyze how these can improve PE2 and DRDC operations. This combination of remote sensing technologies can provide improved knowledge and additional details not provided by using Synthetic Aperture Radar (SAR) imagery alone. DIOS will demonstrate how these types of technologies can benefit detection, discrimination, identification and tracking of ships and icebergs in open water.

DIOS will deliver maritime surveillance tools to PE2 to address a specific capability gap. That gap is in PE2’s ability to monitor ships using RCM with complimentary high resolution X-Band SAR and EO/IR data. The monitoring areas will include ports and coastal areas, where RCM data alone have limitations. The use of multi-satellite imaging data supplemented with the radar altimeters can increase temporal frequency of acquisitions for certain regions. C-CORE’s work scope will deliver algorithms that optimally detect targets then classify those targets into vessels and false alarms. In this context, false alarms may be caused by icebergs, clouds or ocean features. C-CORE proposes that the algorithms developed within DIOS be delivered as a TN so that PE2 software developers can implement the algorithms within OceanSuite without further input from C-CORE’s software developers. In parallel, C-CORE will investigate techniques to implement the algorithms within its own Iceberg Detection Software (IDS) to facilitate an improved commercial SAR surveillance service to its oil and gas (O&G) clients. C-CORE will demonstrate the enhanced detection capability in an operational context with one of its O&G clients. The demonstration

1 In delivering the algorithm as a TN, the project is not disruptive to the existing PE2 software development process. PE2 have already engaged a third party contractor to develop a new version of OceanSuite, which is the tool that PE2 will use to analyze RCM data for ship targets. Thus, it is logical to deliver the algorithm in a form of a TN to that contractor for implementation.



will allow the evaluation of several performance measures on the value of the new innovation, in terms of both detection and classification performance.

The operational ship detection, classification, identification and tracking using multi-satellite data can potentially provide significantly more information about ships than any single type of satellites, but it is a complex problem that requires a solution of several tasks. Capabilities of individual sensors, including different bands and modes, for automated extraction and analysis of features specific for ship and non-ship targets have to be investigated. Automated feature extraction algorithms and methods for target classification and ship identification (i.e. determination of ship size and type) using data from two or more satellites, including ship simultaneous appearance in multi-sensor data, have to be investigated and developed. Automated algorithms for multi-sensor and multi-temporal data analysis and integration into IDS or other prototype software is required for ship/iceberg tracking and false alarm reduction.

The main objectives of the DIOS project include the following:

• To develop a capability for EO/IR sensors to compliment the RCM mission for ship detection, identification, classification and tracking;

• To use TSX, EO/IR and radar altimetry to help contribute new knowledge to DRDC and their stakeholders;

• To investigate new ways of monitoring shipping traffic and icebergs in open water using multiple sensors; and

• To investigate and develop a multi-satellite data classifier to characterize satellite detections as ships, icebergs or other non-ship targets.



2 Project Management (WP 1000) The following subsections provide details on the methodology, the major work packages (WPs) that will be performed during this project and an update on project management items including schedule, budget and deliverables.

2.1 Methodology C-CORE’s integrated methodology for DIOS is shown in Figure 1 and consists of three major steps of data manipulation:

• Satellite data collection; • Pre-processing and target detection; and • Integration, including sensor data fusion, target identification and tracking.

Satellite data collection includes acquisitions previously planned by a satellite operator and tasking for the purpose of DIOS project. Very High Resolution (VHR) satellite data, such as Pleiades and SPOT will be tasked for new acquisitions over the selected study areas. Pre-planned satellite acquisitions will include:

• SAR: Sentinel-1A/1B (S-1A and S-1B); • Medium Resolution (MR) EO/IR: Sentinel-2A/2B (S-2A and S-2B), Landsat-8 (L-8); • Low resolution (LR) EO/IR: MODIS on Aqua and Terra, Suomi NPP, and Sentinel-3 (S-3); and

Altimeters.

Pre-processing techniques will be performed, which includes calibration and cloud masking on the EO/IR data. Target detection algorithms will include CFAR detection with IDS and detections algorithm for EO/IR. Target detection for altimetry data is based on a combination of the Fast-Fourier Transform and thresholding. Recently, C-CORE developed a capability for automated land-based target and change detection using VHR and Medium Resolution (MR) EO/IR satellites that requires minimum analyst input. This technology underwent pre-operational evaluation for corridor monitoring (Zakharov et al. 2016) and is directly relevant to the DIOS initiative.

The final integration stage will include feature extraction and the combination of the target classification algorithm outputs with the tracking information provided by Airbus. The final DIOS algorithm will distinguish ships from non-ship targets.



Figure 1. Flowchart of multi-satellite data integration for ship detection, classification, identification and tracking.

2.2 Work Package Descriptions (WBS) The project objectives will be addressed by executing the tasks shown in Table 1. The following sections describe work conducted under WP 1000, 3000, 4000 and 5000. WP 2000 (Literature/Technology Review) was previously completed and the remaining WPs 6000 - 8000 have not yet commenced. C-CORE is responsible for the overall project management and technical work packages, including WP 2000 to WP 4000, WP 7000 and WP 8000. Airbus is responsible for WP 5000 and WP 6000.

Integration

Satellite data collection

SAR C-Band: S-1A, S-1B, R-2, RCM X-Band:TSX1, TDX1

EO/IR: VHR: Pleiades, SPOT-6, SPOT-7 MR: S-2A, S-2B, L-8 LR: MODIS (Aqua& Terra), Suomi NPP, S-3

Altimeters: Jason-2, Jason-3

Pre-processing & Target Detection

CFAR detection with IDS Detection

algorithm in OE/IR

AID Calibration and cloud masking

Feature extraction and Target Classification

Identification and Tracking

IDS classifier EO/IR classification

Altimetry classification algorithm

Fusion and identification

Ships and non-ship targets

Tracking

AIS



Table 1. Work Packages

Work Package Task Description 1000 Project Management 2000 Literature/Technology Review 3000 Data Collection and Validation 4000 Algorithm Development for Ship Classification 5000 Multi-Sensor Tracking (Airbus) 6000 Ship Identification (Airbus) 7000 IDS Implementation 8000 Results Consolidation

2.3 Schedule and Budget The KOM was held on September 6, 2018 at the Airbus office in Ottawa and the first Progress Meeting was held on January 31st, 2019 at the Valcartier Research Center in Quebec. There are no changes to the baseline schedule. However, WP 3000 – Data Collection and Validation will be extended to July 31st to accommodate better cloud-free conditions for electro-optical imagery collection and WP 5000 will be delayed 2 months until May 1st to better coincide with this change in the field programs. Since the information on ship targets collected in the field will be input into this Airbus WP 5000. An updated project schedule is shown in Figure 2. DIOS is a 23-month project and is scheduled to end on July 31, 2020. Progress meetings will be conducted every four months to allow for frequent review and feedback. The progress review meetings will serve as Go/No-Go points to cancel or redirect project activities, if necessary. The project is currently on track and within budget limits.

Figure 2. Updated Project Schedule

KO = Kick off, PR = Progress Review Meeting, FM = Final Meeting

2.4 Deliverables The project has the following progress meetings and reports scheduled based on the statement of work. A complete list of project management deliverables is provided in Table 2. Progress meeting dates may change and are subject to schedule availabilities of the Project Authorities and Sponsors. The WP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23YearTask Description S O N D J F M A M J J A S O N D J F M A M J JWP1000 Project Management KO PR PR PR PR PR FMWP2000 Literature/Technology ReviewWP3000 Data Collection and ValidationWP4000 Algorithm Development for Ship ClassificationWP5000 Multi-Sensor Tracking (Airbus)WP6000 Ship Identification (Airbus)WP7000 IDS ImplementationWP8000 Results Consolidation

2018 2019 2020



deliverables are presented in Table 3. All associated reports and deliverables will be submitted to DRDC via the project’s SharePoint.

Table 2. List of Project Management Deliverables

Item Deliverable Planned Date Delivery Date/Status MPR Monthly Progress Reports Ongoing Ongoing KOM Kick-Off Meeting September 6, 2018 September 6, 2018 PR1 Progress Review Meeting 1 January 31, 2019 January 31, 2019 D1 Progress Review Report 1 January 24, 2019 January 24, 2019

D2 Progress Review Meeting 1 Agenda, Presentation and Minutes February 8, 2019 February 8, 2019

PR2 Progress Review Meeting 2 May 2019 May 29, 2019 D3 Progress Review Report 2 May 2019 May 22, 2019

D4 Progress Meeting 2 Agenda, Presentation and Minutes May 2019 May 22, 2019

June 5, 2019 PR3 Progress Review Meeting 3 September 2019 D5 Progress Review Report 3 September 2019

D6 Progress Review Meeting 3 Agenda, Presentation and Minutes September 2019

PR4 Progress Review Meeting 4 January 2020 D7 Progress Review Report 4 January 2020

D8 Progress Review Meeting 4 Agenda, Presentation and Minutes

January 2020

PR5 Progress Review Meeting 5 May 2020 D9 Progress Review Report 5 May 2020

D10 Progress Review Meeting 5 Agenda, Presentation and Minutes

May 2020

D11 Final Report July 2020 D12 Final Review Agenda, Presentation and Minutes July 2020 D13 OceanSuite Technical Note July 2020 FRM Final Review Meeting July 2020 D14 Project Close Out July 2020

Table 3. List of Work Package Deliverables

Work Packages Item Deliverable 2000 WP1 Literature Review as input to Progress Report 1 (D1)

3000 WP2 Satellite image acquisitions plans WP3 Field data collection plans WP4 Health Safety and Environmental (HSE) plans



Work Packages Item Deliverable WP5 Datasets for WPs 4000, 5000 and 6000

4000 WP6 Optimized algorithms in the form of documentation for Progress Review Reports (PRRs).

5000 WP7 Report with documented results and demonstration of obtained results (Airbus)

6000 WP8 Report with documented results and demonstration of obtained results (Airbus)

7000 WP9 Lessons learned from the IDS Implementation

8000 WP10 License of IDS for Research Purposes WP11 Operational trial with O&G industry



3 Data Collection and Validation (WP 3000) The objectives of this WP are to collect and validate multi-satellite data ship-like targets (e.g. iceberg, ocean surface and vessels) in Canadian ports. Field programs will be concerted in two ports, first in Halifax, NS and then in St. John’s, NL during the summers of 2019 and 2020. In addition, opportunistic field campaigns will be staged to occur in collaboration with other C-CORE projects that have related field work.

3.1 Plan for Field Data Collections and Ground Truthing Field planning began in February 2019 and will continue to early June until the first field program in Halifax harbour commences. It is anticipated to start the Halifax field program during the week of June 10th and the St. John’s the beginning of July for approximately 7 days. This will allow for sufficient time to collect the field data during the summer months in anticipation that by July 31st all of the field programs will be completed. The resource requirements for these upcoming field programs have been identified and the necessary equipment such as a laptop, digital camera, GPS, etc. have been secured. Prior to these field activities, C-CORE will develop and implement an approved Health Safety and Environment (HSE) plan to cover the field activities. A further recommendation would be to contact the associated Port Authorities to make them aware of these field activities and to obtain approval for site visits to capture photos, etc. The local Port Authorities may also provide additional ground information data that is not presently listed on their websites. C-CORE is recommending that a letter or email be provided by DRDC that introduces the project and its objectives, which can be submitted with this request. This will be followed by the development of the image acquisition plans and the coordination of other auxiliary data, such as the terrestrial-based AIS from the DRDC Order desk.

The initial tasking of the VHR imagery, SPOT-6 and 7, Pleiades and high resolution TSX will be performed in conjunction with Airbus. From this planning, an initial cloud assessment will be performed to determine the potential cloud-free days to collect imagery and based on these windows of opportunity, the field work will then be initiated and C-CORE personnel will travel to Halifax harbour to collect ground truth data on different ship characteristics, such as type, class, approximate size, etc. A sample of a field data sheet that was designed for the field programs is contained in Appendix A. There may also be a need to adjust the field data collection activities with image the acquisition plans where necessary.

Field data will include small vessels that do not utilize AIS transponders, such as wooden vessels. This will aid in developing algorithms which are not biased to a particular ship size. Ground based surveillance data that will also be used include information from Port Operation websites and other marine information websites, such as Marine Traffic.2

Figure 3 shows an example of the St. John’s Port Authority website which shows in near real time (NRT) the ships currently in Port with active AIS transponders.3 Other data that is available on the Port Authorities websites include web cameras, route maps, average dwell time, key performance indicator

2 Marine Traffic website: https://www.marinetraffic.com/en/ais/home/centerx:-52.697/centery:47.564/zoom:15 3 The Port of St. John’s, NL website: https://sjpa.com/about-the-port/ships-in-port/

https://www.marinetraffic.com/en/ais/home/centerx:-52.697/centery:47.564/zoom:15

https://sjpa.com/about-the-port/ships-in-port/



(KPI), GIS maps and real time data that is updated every 15 minutes. An example from the Port of Halifax website4 is shown in Figure 4.

Figure 3. Port of St. John’s, NL Map of active ships in Port. (Taken from Marine Traffic website)

Figure 4. Port of Halifax, Nova Scotia Website

Searches of electro-optical archival imagery were conducted to identify backup locations to obtain ship target information, such as large harbours and marinas with favourable weather conditions. Suitable

4 The Port of Halifax, NS website: https://www.portofhalifax.ca/facilities/

https://www.portofhalifax.ca/facilities/


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locations covered by VHR imagery were identified in the Strait of Gibraltar and Gulf of Mexico. If required, the corresponding imagery will be ordered at a later date.

3.2 Cloud Cover Assessments Several electro-optical satellite sensors were investigated to obtain estimates on cloud cover of the two city ports during the Spring/Summer months of April to July. Sentinel-2 (S-2), MODIS and the Meteosat series of satellites were investigated to obtain cloud statistics, which provided cloud data at medium to course resolutions.

3.2.1 Sentinel-2

The first dataset investigated was the Sentinel-2 (S-2) A and B satellite coverage from 2016-2018. The S-2 imagery used in the analysis was the visible (RGB) and near-infrared (NIR) bands with 10 metre resolution. Sample images for April to July were downloaded and visually inspected for cloud cover. The number of cloud free images were divided by the total number of images to obtain a cloud percentage across each month. There were a total of 38 images for the St. John’s study area and 76 images for the Halifax area. There were more S-2 coverage over the Halifax harbour, since it is a larger port and the southern portion of the harbour was covered by partial S-2 scenes. The analysis revealed that more cloud-free imagery were available over Halifax during the time period under consideration. For SJ, the largest proportion of cloud-free images were acquired June and July with 40% and 58%, respectively. In the case of Halifax, the months with the most cloud-free images were April (67%) and June (61%). Figure 5 and Figure 6 show sample cloud-free S-2 images of both study areas from this analysis.


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Figure 5. Cloud-free Sentinel-2A image of Halifax, Nova Scotia captured on July 23, 2017


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Figure 6. Cloud-free Sentinel-2A image of St. John’s, NL captured on July 26, 2018

3.2.2 MODIS

In addition to Sentinel-2, courser resolution MODIS imagery was used to retrieve cloud statistics. To this end, 1-km global cloud cover products5 for April, May June and July covering a 15-year period (2000 - 2014) were reviewed. The MODIS-derived monthly cloud frequencies for the two study areas are presented in Figure 7. The cloud cover characteristics are similar for both areas, suggesting a probability of approximately 60% of obtaining imagery affected by cloud cover any given month. For St. John’s, June and July show the lowest cloud frequencies, while the lowest likelihood of cloud cover over Halifax occurs in April and July. These results align with the cloud cover estimates retrieved from S-2 data.

5 MODIS Cloud Cover products available at: https://www.earthenv.org/cloud

https://www.earthenv.org/cloud


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Figure 7. MODIS Cloud Cover Frequency for April – July from 2000-2014 for two study areas.

3.2.3 Meteosat

EUMETSAT operates a fleet of meteorological and climate monitoring satellites and processes and disseminates data and products. Cloud cover characteristics were estimated by reviewing fractional cloud cover data provided by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was. The environmental data records for April to July of 2016-2018 were retrieved from CM SAF website6. The spatial resolution of cloud products is approximately 3 km at nadir. Figure 8 to Figure 10 present cloud products generated over the study areas in April and May from 2016 to 2018. Overall, Halifax shows a lower cloud fraction in June-July each year than St. John’s. The lowest cloud fractions for St. John’s were observed in May (2018) and July (2017, 2018).

6 CM SAF website: wui.cmsaf.eu


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April 2016 May 2016 June 2016 July 2016

Figure 8. Metosat Cloud Cover Percentages for April – May 2016




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4 Algorithm Development for Ship Classification (WP 4000)

4.1 Ship Detection in Medium Resolution EO/IR data

4.1.1 Sentinel-2 Imagery

Sentinel-2 data can be provided with different product levels. During this analysis, Level-1C and Level-2A were tested. Level-1C is orthorectified product providing top-of-atmosphere reflectances (Gatti and Galoppo 2018). Level-2A is orthorectified product providing surface reflectances, and basic pixel classification (including classes for different types of cloud). The scene classification algorithm allows detection of clouds, snow and cloud shadows and generation of a classification map, which consists of three different classes for clouds (including cirrus), together with six different classifications for shadows, cloud shadows, vegetation, not vegetated, water and snow (ESA n.d.). Level-1C data can be converted to atmospherically corrected Level-2A product by using ESA software Sen2Cor.

The algorithm for ship detection is shown in the following diagram (Figure 11).

Figure 11. Flowchart of ship detection algorithm using Sentinel-2 data.

The first step is the sea-land separation (land masking) where land areas are removed from the image. Sentinel-2 data product Level-2A includes a cloud mask. Cloud removal can also be performed by identifying cloud-free pixels using Band 10 (1375 nm) and Band 11 (1610 nm). Cirrus clouds are thin, transparent or semi-transparent clouds, forming at high altitudes, approximately 6-7 km above the Earth's surface. The method of identifying cirrus cloud pixels from dense cloud pixel is based on Band 10, which corresponds to a high atmospheric absorption band (ESA n.d.). Dense cloud reflectance is high in the blue Band 2 and short wave infrared (SWIR) (Band 11 and Band 12). After detection both dense and cirrus clouds morphology-based operations are applied to remove isolated pixels and to fill the gap and extend clouds. In this project the generated Level-2A cloud mask is used.

The adaptive threshold approach for target detection is implemented as a constant false alarm rate (CFAR) algorithm and it is frequently used for detecting targets in open water. To reduce environmental effects the NIR Band 8 (842 nm, 10m resolution) is used for detection. As it is shown in Figure 12 the probability distribution function is close to the Gaussian distribution for the ocean in EO/IR data. Target detection was performed using cell averaging (CA) CFAR. Implementation of this approach can be conveniently


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performed by configuring three windows, called target, guard and background windows. The size of the target window is usually selected to be comparable to the size of the smallest ship of interest. The size of the guard window has to be larger than the target window to avoid the presence of a ship signal in the background window. The size of background window has to be sufficient to estimate the local statistics. Using these windows each pixel in the Sentinel-2 image can be tested with the CFAR detector. The detector returns all target pixels with intensities larger than the calculated background mean multiplied by a threshold.

Figure 12. Image of Band 8 with ships in the ocean (left) and the corresponding histogram (right).

Ship detection in EO/IR imagery significantly depends on sea state and cloud conditions. Figure 13 demonstrates detection results using the CA CFAR technique in cloud-free conditions, but with the presence of waves. Band 3 (560nm) of Sentinel-2 (2019-03-25) was used for detection. The usage of other visual bands (10m resolution), such as Band 2 (490nm) and Band 4 (665nm), generated similar results. Moving windows of different sizes were defined as follows:

• Target detection: 21 pixels; • Guard window: 51 pixels; and • Background estimation: 75 pixels (in pixels).

This configuration helped successfully detect 31 of 32 ships with no false detections. Examples of detected ships are shown in Figure 14. The length of the ship (on the right) is about 400m and it was measured


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using PCI Geomatica. The RGB Sentinel-2 image, shown in Figure 15, contains many small clouds and waves. Figure 16 demonstrates examples of waves in RGB image. The waves can have various shapes and patterns, generating a potentially large number of false alarms. In order to reduce the number of false alarms, the detected vessel candidates have to be discriminated from waves and clouds. The wave presence can be mitigated by using SWIR bands B11 (1610nm) or B12 (2190nm).

Figure 13. Detection of ships near the Strait of Gibraltar in Sentinel-2 image (2019-03-25) using CA CFAR technique. The width of image fragment is 110km.

Figure 14. Examples of ships detected in a Sentinel-2 image. The length of the ship on the right is approximately 400 meters and the other ships are comparable sizes.


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Figure 15. Sentinel-2 RGB Image (2019-03-23) and cloud mask created using Band 10 (1375nm).


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Figure 16. Sentinel-2 RGB Image (top) and Band 11 (1610nm) (bottom) with ships and small clouds.

4.2 Ship and Cloud Discrimination The presence of small clouds is still a significant problem because isolated clouds may look like vessels. In order to solve the problem of small clouds discrimination the classification algorithms were implemented


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and tested. The purpose of ship-cloud discrimination is to retain true ship from all detected targets. The results of discrimination are targets of two types’ of ships and non-ships (clouds, small islands etc.). Sometimes there is no obvious discrimination between ships and non-ships when false alarms have the same shape and dimension of a ship (Kanjir, Greidanus, and Oštir 2018) and cannot be removed.

4.2.1 Feature Selection

The discrimination task requires selecting specific features appropriate to two classes of targets. To improve the quantitative discrimination between ships and clouds (non-ships) the use of the several geometrical features was examined. Each detected target was described by the following seven features (MATLAB, 2018) describing ship signatures for Sentinel-2 data:

1. Area: the number of pixels in the target. 2. Eccentricity: the ratio of the distance between the foci of the ellipse (that has the same second-

moments as the target) and its major axis length. 3. Solidity: the proportion of the pixels in the convex hull that are also in the target (computed

as Area/ConvexArea). 4. Extent: the ratio of pixels in the region to pixels in the total bounding box (computed as the

Area divided by the area of the bounding box). 5. Major Axis Length: the length of the major axis of the ellipse that has the same normalized

second central moments as the target. 6. Minor Axis Length: the length of the minor axis of the ellipse that has the same normalized

second central moments as the target. 7. Ratio of major to minor axes.

All seven features were statistically analyzed to understand their contribution to discriminating between ships and clouds. All features were analyzed as predictor variables in the classification algorithms.

4.2.2 Collecting Training Data

Data for training and validating classification algorithms were collected in the Straight of Gibraltar containing ships (vessels) and clouds. After screening and quality control of the detection results, 265 samples representing 104 ships and 161 clouds were selected for subsequent analysis. This dataset was further divided into random samples for classifier training (65%) and validation (35%).

4.2.3 Classification Algorithms

The selection of suitable classification algorithms was based on examining the statistical properties of the training data. Only supervised classification approaches were considered in this research. A recent experiment with different classification algorithms for altimetry data (Zakharov et al. 2017) compared Decision Tree (DT), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Neural Network (NNet), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN). Among all of these classifiers, NNet and SVM are popular approaches from the field of machine learning but they require more effort for training than Discriminant Analysis. The SVM algorithms demonstrated the best performance for discriminating two classes of targets within a 22-dimensional feature space (Li, Azimi-


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Sadjadi, and Robinson 2004). However, the performance of these classifiers is dependent on the application and the classifier parameters selected. A detailed description of the classifiers can be found in the numerous references, for example in (Bishop 2006; Vapnik 1998). As the result of analyzing different classifiers and their parameters the following classifiers were selected:

Linear Discriminant Analysis (LDA) assigns test targets with the highest posterior probability computed using the maximum likelihood rule.

Quadratic Discriminant Analysis (QDA) is a discriminant function with quadratic decision boundaries which can be used to classify datasets with two or more classes. QDA has more predictability power than LDA. Here, validated iceberg and ship targets were used in a supervised training methodology where features were tested and evaluated as input into the quadratic discriminant models.

Support vector machines (SVM) are based on the concept of decision planes that define decision boundaries between classes. A decision plane is one that separates sets of objects having different class memberships. An SVM classifier with Radial Basis Function (RBF) kernel function to map the training data into kernel space was used to classify ships and icebergs.

K-Nearest Neighbour (KNN) method assigns targets to the class having the majority in the k nearest neighbours in the training set. For k=1 only one nearest object in the training set is used.

Neural Networks (NNet): a layered perceptron type artificial neural network was tested. This is a standard feedforward network widely used for pattern recognition tasks. It has already been successfully applied to discriminate ocean targets (Tulyakov et al. 2008). The network consists of one input layer, 55 hidden layers, and one output layer.

Decision Tree (DT) classifier is a simple classification technique based on the application of a series of the questions in a tree structure predicting responses to data. Classification trees give responses that are nominal, such as 'true' or 'false'.

Combined Classifiers: the advantage of combining classifiers can be explained by the diversity of the classifiers’ strengths on different input features. Each classifier may perform particularly well on certain types of input patterns. Simple approaches for combining classifiers considered include the unweighted, confidence voting and majority-rule methods (Van Erp, Vuurpijl, and Schomaker 2002). The majority rule was used to identify class for each target.

The error was calculated as an average value of classification loss, which is the rate (with maximum 100%) of misclassification for targets not used for training. Figure 17 shows that several classifiers achieved classification accuracies close to 100% using Sentinel-2 data acquired in March-April 2019 over the Strait of Gibraltar. It is notable that the proportion of the sample population used to train the algorithm can be dependent on geographic location and the season. Small targets typically produce challenges for classification as the target signatures for both classes can be very similar.


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Figure 17. Classification accuracies (ship/non-ship classification and misclassification rates) for all classifiers.

Several classifiers were tested on the Sentinel-2 image acquired on April 12, 2019. The best results were achieved using QDA classifier (Figure 18). Blue circles indicate small clouds and green triangles show ships. In total 37 ships and 65 clouds were classified. However three ships were misclassified as clouds and two clouds were misclassified as ships.


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Figure 18. Map of detected targets in Sentinel-2 image after discrimination using QDA: green triangles represent ships and blue circles indicate clouds.

4.2.4 Landsat 8 Imagery

Ship detection in Landsat-8 imagery was conducted using the similar algorithm as with Sentinel-2 data (the flowchart is shown in Figure 11). Landsat-8 Level 1 GeoTIFF data products contain an image for each corresponding band and associated metadata file. The data products consists of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data acquired. The data products are delivered in 16-bit unsigned integer format and need to be rescaled to Top of Atmosphere (TOA) Reflectance and Brightness Temperature (BT) before processing begins. This conversion is performed using mathematical formulas available from the USGS webpage. Once the conversion has taken place, the TOA and BT values can then be used as input for the algorithm. Cloud information is included in pre-collection Band of Quality Assessment (BQA). Each pixel in BQA contains information that represent combinations of surface, atmosphere and sensor conditions. Cloud detection can also be performed using a modified version of an algorithm developed by Zhu and Woodcock (2012). This algorithm is built on the results of previous approaches, such as automated cloud-cover assessment (ACCA) algorithm (Irish et al. 2006). An example using the modified ACCA algorithm is shown in Figure 19. A cloud detection algorithm (Luo, Trishchenko, and Khlopenkov 2008) was also tested with Landsat-8 data.

Target detection was performed with the CA CFAR algorithm on the panchromatic band B8 (503 – 676nm) with 15m resolution. The short wave infrared band, B6 (1566-1651nm) with 30m resolution was used to


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discriminate ship targets from ocean features. Testing on the Landsat-8 data acquired on April 13, 2019 over the Gibraltar area resulted in 373 detected targets in total. Among them there were 270 ocean features. Classification of the remaining 103 targets produced 67 ships and 36 small clouds (Figure 19). Nine targets (most of them are small boats) were misclassified as clouds. One cloud was misclassified as a ship.

Figure 19. Detection in Landsat-8 imagery (2019-04-13): cloud mask generated using modified ACCA algorithm (on the left) and ship (green triangles) and cloud (blue circles) classification results (on the right). The image width is about 190km.

4.3 VHR Data: Testing on WorldView-2 Data Using VHR WorldView-2 image over Sydney Harbour in Australia, two detection algorithms were compared, including adaptive thresholding and a saliency-based approach. The adaptive implementation of the CA CFAR technique was also used for detection. For ideal conditions, such as calm sea and cloud free weather, global thresholding performs well. However, in the case of more complex sea state, adaptive thresholding is expected to provide better results. The saliency detection algorithm is based on local kernels (local descriptors) from the given image, which measure the likeness of a pixel to its surroundings (Seo and Milanfar 2009). The key idea behind the local steering kernels is to robustly obtain the local structure of images by analyzing the radiometric differences based on estimated gradients. The framework results in a saliency map where each pixel indicates the statistical likelihood of saliency of a feature matrix given its surrounding feature matrices.

Figure 20 demonstrates the detection results achieved using the adaptive thresholding and the saliency-based approach. The adaptive thresholding provided better performance by detecting more small targets and it also works faster. In total the adaptive thresholding detected 72 ships and the saliency-based approach detected 54 ships. The saliency-based approach missed many boats in the high density areas.


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Some boats were missed with the adaptive thresholding at the edges of the image due to window implementation of the algorithm. In this case the target detection is performed on a slightly smaller image.

Figure 20. Detection results for WorldView-2 image (on the left) over Sydney Harbour achieved using the adaptive thresholding (on the right) and saliency-based approach (in the middle). Image width is 1km.


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4.4 Low Resolution EO/IR Data

4.4.1 Day-Night Band of Suomi NPP satellite

Figure 21. Ship (bright point) near the Labrador coast in DNB Suomi-NPP VIIRS on August 18, 2018 (image courtesy NASA and NOAA).

Maritime commercial ships also operate lights that can be detected from space (Straka et al. 2015). The Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) satellite carries a Day-Night Band (DNB) radiometer. The VIIRS DNB uses a sensing technique designed to capture low-light emissions (at 0.4-1 micron). Due to S-NPP’s polar orbit and the DNB’s wide swath (~3040 km), the same location in Polar Regions can be observed for several successive passes via overlapping swaths—offering an ability to track ship motion. An example of the highlighted ship near the Labrador coast on August 18, 2019 is shown in Figure 21.

4.5 Target Detection Using Altimetry Data Since 2011, C-CORE has used altimetry data to automatically detect and monitor icebergs around Antarctic for yacht navigation during races. In a 2013-2014 project for the British Antarctic Survey (C-CORE, 2014), historical altimetry data from 1991 to 2013 were used to detect icebergs and evaluate iceberg presence in the southern Ocean.


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Satellite radar altimetry, operating in C- and Ku-bands, is capable to detect marine targets such as icebergs and ships (Tournadre 2007). It was used for iceberg detection and mapping in Antarctica (Tournadre et al., 2016). Current NASA altimeters (Jason-2 and Jason-3) were used for ship detection. Processing and analyzing altimetry data can be performed automatically. Altimetry signatures of iceberg and ship are simultaneously recorded in Ku- and C- radar bands (Figure 22).

Figure 22. Example of signatures of an iceberg (left) and a ship (Right) in Ku- and C- bands.

The detection of the signature, which has a parabola-like shape, is based on the template matching method. The ship appearance is generally different from iceberg because ship infrastructure generates more complex scattering. In order to discriminate ships from icebergs, several classification techniques were applied to achieve accuracy 80-90% (Zakharov et al., 2017).

Probability of iceberg detection by one altimeter depends on latitude and it can be up to 50% for latitudes higher 63o N. Figure 23 demonstrates ground tracks of NASA/CNES satellite altimeters operating between latitudes of 66o S and 66o N for a ten-day cycle. The border at latitudes near 66o N, where the altimeter has a very dense coverage, can serve as a “Gateway” for the detection of marine targets. A swath which captures targets depends on sea state and can be up to 40 km wide. With several available altimeters the detection probability can be higher. The advantages of the usage of altimetry data are:

• Global coverage; • Possibility of providing unique ship signatures simultaneously captured in two radar bands; • All-weather and all-season operation.


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Figure 23. Ground tracks of Jason-3 altimeter for one repeat cycle (10 days). A red rectangular indicates an area used for target detection.

In order to evaluate performance of altimetry based technique in Northern Hemisphere the detection was performed in the area (shown as a red rectangular in Figure 23) between latitudes 45oN to 66oN and longitudes 45oW to 65oW. The detection results for August 2018 are shown in Figure 24. In total 66 targets were detected. Higher number of targets corresponds to the areas with high density of ground tracks (60o-66oN latitudes).


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Figure 24. Targets detected with Jason-2 (black triangles) and Jason-3 (blue triangles) altimeters in August 2018.

Based on manual visual interpretation of altimetry signatures of iceberg and ship (examples are shown in Figure 22) it was possible to classify target detection results. The classification results are shown in Figure 25 as stars for ships and triangles for icebergs.


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Figure 25. Targets classified as ships (stars) and icebergs (triangles) in August 2018.


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5 Multi-Sensor Tracking (WP 5000) During the Progress Meeting 1 in January 2019, results of an older study were presented where vessel features were extracted from roughly 100 satellite EO images in different maritime areas. The main finding was that the vessel dimensions, length (L) and width (W), as well as the vessel types and even sub-types can be rather accurately determined from EO images by image analysts. Table 4 provides a list of all vessel types and sub-types extracted from these EO images.

Table 4. Vessel types extracted from satellite EO images.

No. Designator Description Comment 1 MYAC Motor yacht 2 PB Patrol boat, general

3 SLOOP Sloop Sailing boat with one head sail

4 SPDBAT Speedboat 5 TM Merchant ship, general 6 TMA Merchant ship, dry cargo, break bulk 7 TMB Merchant ship, bulk carrier 8 TMC Merchant ship, container (not-self-sustained) 9 TME Merchant ship, RO/RO Roll-on/ Roll-off 10 TMO Merchant ship, tanker 11 TMOS Merchant ship, special liquids 12 TMP Merchant ship, passenger 13 TMT Merchant ship, tug (ocean-going) 14 TU Fishing vessel, general 15 Unknown Unknown vessel 16 YB Barge, non-self-propelled

A statistical analysis of the L/W ratio as a function of the extracted vessel type (see Figure 1, left) revealed that this ratio might be a suitable parameter to distinguish between different categories of vessel types. Firstly, the scatter of ratios ((L/W)sigma) per vessel type is smaller than the scatter of lengths and widths relative to the corresponding mean values. Secondly, this ratio seems to be constant across the different sub-categories TM,…,TMO of merchant ships as well. And thirdly, L/W=5 might be a useful threshold ratio that is capable of distinguishing smaller merchant and government vessels from larger merchant vessels. A subsequent Monte Carlo simulation of L/W sample ratios for all extracted vessel types has confirmed that this distinction is possible with a rather large accuracy (75%).

To see if the same classification into main vessel categories can also be done based on SAR images, we have done a similar analysis of statistical properties of L/W ratios in this project. To this end, we have used historical TerraSAR-X images that were acquired over several but mostly different areas than those of the EO images. Roughly 100 of the SAR images were associated to AIS tracks and the corresponding AIS data


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were available to us. In these cases, it was possible a) to determine the type of the vessel (from the AIS data) and b) to compare the vessel dimensions extracted from the SAR images to the dimensions given in the AIS data and to verify if there is a bias in the dimensions that were extracted from the images. For some image frames this turned out to be true (e.g. in one frame the width was overestimated by 8% and in another the length was overestimated by 20%). The biased vessel dimensions were compensated for the evaluated mean biases and used in the L/W statistics together with the unbiased extractions. This is displayed in Figure 26 (right) were all vessel types have been derived from the associated AIS data. The figure shows that also for SAR images it is possible to extract L/W ratios and use them to distinguish large merchant ships from small ones and from recreational ships.

Figure 26. L/W ratios extracted from EO images (left) and SAR images (right) as a function of vessel type

The result raises the question, if it is possible to use the following complementary EO and SAR image data to get a rough estimate of a vessel’s volume:

• Vessel type and maybe also sub-type (EO); • L/W ratios (EO, SAR); • Radar Cross Section (SAR); and • Type and number of vessel’s superstructures (EO).

In a first step, it would be helpful to estimate whether a vessel’ s volume is smaller or larger than 300 gross tons since this is the threshold above which ships engaged in international voyages have to use AIS to report their positions and voyage related data in a regular manner. If the combined EO and SAR image extractions of a vessel indicate that the vessel’s volume is above this threshold but there is no associated AIS signal over an extended period of time, then this constitutes not only an anomaly in the sense of unusual behavior but an anomaly in the sense of unlawful behavior of the vessel. This kind of result would be very useful in distinguishing unusual from unlawful behavior and in tagging the vessel accordingly. The field campaign planned in summer 2019 is expected to provide EO and SAR image data that can be used to investigate this question in more detail.


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6 Future Work The next steps in the DIOS project under WP 3000 will involve the collection and validation of multi-satellite data ship-like targets (e.g. ocean surface features, iceberg, and vessels) in St. John’s and Halifax. Data collection for icebergs may also include other potential field campaigns in collaboration with other C-CORE projects or opportunistic field work. The immediate tasks for WP 3000 will include the collection of very high resolution and medium resolution EO/IR and X-Band SAR data will be conducted over ports of St. John’s and Halifax. During satellite data collection the C-CORE analysts will gather ground validation information on ships by capturing field photographs, the use of webcams and other auxiliary information.

The immediate tasks for WP 4000 and 5000 will include the following:

• Performance of low resolution EO/IR sensors can be demonstrated with ship voyages of FedNav (previous or new). The X-Band data acquired by TSX satellite and C-Band RS-2 data can also be used for validation;

• Freely available altimetry data acquired by Jason-2/3 altimeters were be obtained and analyzed over Baffin Bay and the Labrador Sea. The TSX and RS-2 data can also be used for validation; and

• Develop a semi-automated algorithm for ship detection close to land, in harbours and ports; • Use extracted L/W ratios together with other complementary EO and SAR features, (e.g. Radar

Cross Section, Vessel type); • Try to estimate the vessel’s volume (smaller or larger than 300 gross tons); • Use this method to detect ships that should have reported through AIS; and • Validate the method with new imagery collected from planned field campaigns during the

summer of 2019.


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7 DIOS Project Results Relevant to DND and CAF It is anticipated that the final results from the DIOS project will provide recommendations in accordance with DND and CAF space-based surveillance requirements (see Appendix B). To date, there have not been any gaps identified with respect to the stated requirements in this report. However this will be reviewed on an ongoing basis throughout the project and documented in each progress report. The DIOS results will contribute to the following items in this report.

Algorithm development has to produce results capable to satisfy Space-Based Surveillance (SBS) Requirements (DND 2017), such as:

• [Req 100.1] Multi-Role Operational Surveillance Capability. The SBS system could comprise of SAR, AIS, EO/IR, HSI, Signals Intelligence and other complementary sensors.

• [Req 202.2] Ship Detection Close to Land. The SBS system is required to have semi-automatic ship detection close to land, in harbors and ports.

• [Req 204.1] Vessel Detection Parameters. The SBS system is required to automatically detect vessels of 15 meters length and larger with a 90% probability of detection and a very low false alarm rate (less than 2.5(10)-9), in all weather conditions up to and including sea state 5 Beaufort Wind Scale 6 (wave height 3 to 4m), for all of the Maritime Areas defined in Annexes A and B of (DND 2017)

• [Req 204.3] Detection Performance in Ice. The SBS system is required to have the same automatic ship detection performance as stated above, even for areas where icebergs are present and where ships may be breaking ice.


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8 References Bishop, Christopher M. 2006. Pattern Recognition and Machine Learning. Information Science and

Statistics. New York: Springer. C-CORE. 2014. “Iceberg Risk Modeling on the Falklands Shelf.” Report 14-033–1137. C-CORE. DND. 2017. “Department of National Defence and Canadian Armed Forces Space-Based Surveillance

Requirements. DND SBS DRD - V1.0.” ESA. n.d. “Sentinel-2 MSI Technical Guide.” European Space Agency. Accessed April 17, 2019.

https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi. Gatti, A, and A Galoppo. 2018. “Sentinel-2 Products Specification Document.” ESA.

https://sentinel.esa.int/documents/247904/685211/Sentinel-2-Products-Specification-Document.

Irish, Richard R, John L Barker, Samuel N Goward, and Terry Arvidson. 2006. “Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) Algorithm.” Photogrammetric Engineering & Remote Sensing 72 (10): 1179–1188.

Kanjir, Urška, Harm Greidanus, and Krištof Oštir. 2018. “Vessel Detection and Classification from Spaceborne Optical Images: A Literature Survey.” Remote Sensing of Environment 207 (March): 1–26. https://doi.org/10.1016/j.rse.2017.12.033.

Li, Donghui, Mahmood R Azimi-Sadjadi, and Marc Robinson. 2004. “Comparison of Different Classification Algorithms for Underwater Target Discrimination.” IEEE Transactions on Neural Networks 15 (1): 189–194.

Luo, Yi, Alexander P Trishchenko, and Konstantin V Khlopenkov. 2008. “Developing Clear-Sky, Cloud and Cloud Shadow Mask for Producing Clear-Sky Composites at 250-Meter Spatial Resolution for the Seven MODIS Land Bands over Canada and North America.” Remote Sensing of Environment 112 (12): 4167–4185.

MATLAB. 2018. “Region and Image Properties.” https://www.mathworks.com/help/images/pixel-values-and-image-statistics.html.

Seo, H. J., and P. Milanfar. 2009. “Static and Space-Time Visual Saliency Detection by Self-Resemblance.” Journal of Vision 9 (12): 15–15. https://doi.org/10.1167/9.12.15.

Straka, William, Curtis Seaman, Kimberly Baugh, Kathleen Cole, Eric Stevens, and Steven Miller. 2015. “Utilization of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band for Arctic Ship Tracking and Fisheries Management.” Remote Sensing 7 (1): 971–89. https://doi.org/10.3390/rs70100971.

Tournadre, J. 2007. “Signature of Lighthouses, Ships, and Small Islands in Altimeter Waveforms.” Journal of Atmospheric and Oceanic Technology 24 (6): 1143–1149.

Tournadre, J., N. Bouhier, F. Girard-Ardhuin, and F. Rémy. 2016. “Antarctic Icebergs Distributions 1992–2014.” Journal of Geophysical Research: Oceans 121 (1): 327–349. https://doi.org/10.1002/2015JC011178.

Tulyakov, Sergey, Stefan Jaeger, Venu Govindaraju, and David Doermann. 2008. “Review of Classifier Combination Methods.” In Machine Learning in Document Analysis and Recognition, 361–386. Springer.

Van Erp, Merijn, Louis Vuurpijl, and Lambert Schomaker. 2002. “An Overview and Comparison of Voting Methods for Pattern Recognition.” In Frontiers in Handwriting Recognition, 2002. Proceedings. Eighth International Workshop On, 195–200. IEEE.


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Vapnik, Vladimir Naumovich. 1998. Statistical Learning Theory. Adaptive and Learning Systems for Signal Processing, Communications, and Control. New York: Wiley.

Zakharov, I., T. Puestow, A. Fleming, J. Deepakumara, and D. Power. 2017. “Detection and Discrimination of Icebergs and Ships Using Satellite Altimetry.” In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 882–85. https://doi.org/10.1109/IGARSS.2017.8127093.

Zakharov, Igor, Paul Adlakha, Thomas Puestow, Desmond Power, Sherry Warren, and Mark Howell. 2016. “Monitoring Pipeline Rights of Way Using Optical Satellite Imagery.” In 11th Pipeline Technology Conference 2016. Berlin, Germany: EITEP Institute.

Zhu, Zhe, and Curtis E. Woodcock. 2012. “Object-Based Cloud and Cloud Shadow Detection in Landsat Imagery.” Remote Sensing of Environment 118 (March): 83–94. https://doi.org/10.1016/j.rse.2011.10.028.


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Appendix A Sample Field Data Sheet

Date (y/m/d): Time: Cloud Conditions: Clear Partly Cloudy Moderate Overcast Overcast

Ship Number Ship Name and Type Length (m) Breadth Extreme

(m) Features: Picture # Look Direction Comments Data: N S

E W

Ship Orientation (°)

Ship GPS Point (Decimal Degrees) GPS Position GPS coordinates Elevation Comments

Front W N

Back W N

Sketch:

Information for Ground Truth data collection form (Intended to be printed on back of the field sheet) Date… Time (specify time zone, local, UTC) Cloud Conditions: Circle One Clear…………………………less than 5% cloud cover Partly Cloudy………………..5-40% cloud cover Moderate Overcast……….….40-80% cloud cover Overcast………………….…..80-100% cloud cover Ship Number: the nth ship that is documented on that day Ship Type: Cargo, Roll on Roll off (i.e. Ferries), Oil Tanker, Passenger Ship, Fishing Vessel, High Speed Craft, Naval Ship, Icebreaker, other type. Ship Name: usually written on ship, an example of a name would Henry Larson, which is an Icebreaker. (If name is not seen, use N/A). Check other category in Ship Type Ship Length/Width: approximate length/width Photograph Data: Pictures of the ship should be taken with some distinguishing features in background and should be taken from a North, South, East, and West perspective of the area. Picture Orientation: the direction from which the picture was taken Ship Orientation: Orientation from magnetic North Ship GPS: Should be collected using the Geographic Coordinate System, WGS84. One GPS position recorded at the front and one at the rear of the ship using a handheld GPS. Sketch and Comments: sketch the ship and surrounding features such as whether the ship is docked, distance from the shore, buildings and roads up to a 30m radius. Also, mark the GPS positions on the sketch. It is very important to identify local surroundings in the comments section.


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Appendix B DND and CAF Report

Department of National Defence and Canadian Armed Forces

Space-Based Surveillance Requirements

Version 1.0

28 February 2017

UNCLASSIFIED

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pp p

SIGNATURE PAGE

Prepared by:

ieutenant-Commander Chad Kabatoff DG Space — ISR Staff Officer

Prepared by:

Dr. Paris W. Vachon DRDC — Senior Scientific Advisor

Prepared by:

S S

S

Maj| isti| E [s trackerjan CFINTCOM — Director of Intelligence » Concepts & Force Development F

A Prepared by:

Lieutenant-Commander Michael R. Bielby CJ OC — J3 North America

Recomgiended y: _

’ effrEy“RTDo‘6ling DGpSpace — DSR -

Approved by:

B igadier-General Blaise F. Fravvley DG Space

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Table of Contents

Introduction .................................................................................................................................................. 1

Governance ................................................................................................................................................... 2

Context .......................................................................................................................................................... 3

Layout............................................................................................................................................................ 4

Generic SBS Requirements ........................................................................................................................... 5

Operational SBS Capability........................................................................................................................ 5

[Req 100.1] Multi-Role Operational Surveillance Capability ................................................................ 5

[Req 100.2] Global Access ..................................................................................................................... 6

[Req 100.3] Efficient Data Processing, Exploitation, and Dissemination (PED) .................................... 6

[Req 100.4] Interoperability .................................................................................................................. 6

[Req 100.5] 24/7 Operability ................................................................................................................ 6

[Req 100.6] RCM Continuity.................................................................................................................. 6

Whole of Government Operations ........................................................................................................... 7

[Req 101.1] Support for DND/CAF and other GC Operations ............................................................... 7

[Req 101.2] WoG Interoperability ......................................................................................................... 7

Req [101.3] Compliance with GC Directives & Policies ......................................................................... 7

System-of-Systems Operations ................................................................................................................. 8

[Req 102.1] System-of-Systems Capability ........................................................................................... 8

[Req 102.2] Ally System Tasking ........................................................................................................... 8

Responsive Planning & Ordering .............................................................................................................. 9

[Req 103.1] Real-Time Collection Planning ........................................................................................... 9

[Req 103.2] Low-Latency Ordering ....................................................................................................... 9

[Req 103.3] System Priority Schema with Feedback .......................................................................... 10

[Req 103.4] Emergency Override Priority ........................................................................................... 10

Low-Latency PED ..................................................................................................................................... 11

[Req 104.1] Global Latency ................................................................................................................. 11

Protection & Security Measures ............................................................................................................. 12

[Req 105.1] Security & Protection Measures ...................................................................................... 12

[Req 105.2] Unclassified & Classified Operations ............................................................................... 12

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[Req 105.3] Secure Up/Downlinks ...................................................................................................... 12

[Req 105.4] Jamming, Blinding, or Interference ................................................................................. 12

[Req 105.5] Ground Infrastructure & Network Security ..................................................................... 12

[Req 105.6] SBS System Manoeuvrability ........................................................................................... 12

Data Sharing ............................................................................................................................................ 13

[Req 106.1] Data Product Sharing ....................................................................................................... 13

[Req 106.2] Network Connectivity for Sharing ................................................................................... 13

[Req 106.3] Data Format ..................................................................................................................... 13

Networked Interface ............................................................................................................................... 14

[Req 107.1] Product Ordering & Delivery ........................................................................................... 14

[Req 107.2] Network Bandwidth ........................................................................................................ 14

[Req 107.3] Cross-Domain Connectivity ............................................................................................. 14

Archive .................................................................................................................................................... 15

[Req 108.1] Data Archive .................................................................................................................... 15

[Req 108.2] Accessibility of Data Archive ........................................................................................... 15

[Req 108.3] Data Plan ......................................................................................................................... 15

[Req 108.4] Data Archive Connectivity ............................................................................................... 15

[Req 108.5] Big Data Exploitation ....................................................................................................... 16

[Req 108.6] System Enduring Capability ............................................................................................. 16

[Req 108.7] Archive Life ...................................................................................................................... 16

Hybrid Classification Operation .............................................................................................................. 17

[Req 109.1] System Security Classification ......................................................................................... 17

[Req 109.2] Restricted Visibility .......................................................................................................... 17

System Life .............................................................................................................................................. 18

[Req 110.1] Space Mission Life ........................................................................................................... 18

[Req 110.2] Ground Segment Life ....................................................................................................... 18

Space-Based Surveillance System Training ............................................................................................. 18

[Req 111.1] Overall Training ............................................................................................................... 19

[Req 111.2] Tailored Training .............................................................................................................. 19

Maritime Surveillance ................................................................................................................................. 20

SAR Surveillance of North American & Arctic Maritime AOIs ................................................................. 20

[Req 200.1] AWAS coverage of North American and Arctic AOIs ....................................................... 20

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vii UNCLASSIFIED DND SBS DRD - V1.0 - 28 February 2017

[Req 200.2] Ship/Ice Discrimination ................................................................................................... 20

SAR Surveillance of DND’s Global Maritime Surveillance AOIs .............................................................. 22

[Req 201.1] AWAS coverage of DND’s Global Maritime Surveillance AOIs ........................................ 22

[Req 201.2] Concurrent Surveillance .................................................................................................. 22

[Req 201.3] Contiguous SAR Swath Coverage .................................................................................... 23

[Req 201.4] Capacity for 50% Growth of Global AOIs ......................................................................... 23

Global Maritime Static Facility AOIs ....................................................................................................... 24

[Req 202.1] Static Maritime Facilities ................................................................................................. 24

[Req 202.2] Ship Detection Close to Land ........................................................................................... 24

[Req 202.3] Rapid Beam Mode Switching ........................................................................................... 24

Naval Task Group (TG) Surveillance Zones ............................................................................................. 25

[Req 203.1] Ship Detection of Naval TG Surveillance Zones ............................................................... 25

[Req 203.2] Direct Satellite Tasking .................................................................................................... 25

Vessel Detection Performance – Wide Area ........................................................................................... 26

[Req 204.1] Vessel Detection Parameters .......................................................................................... 26

[Req 204.2] Atmospheric Conditions .................................................................................................. 26

[Req 204.3] Detection Performance in Ice .......................................................................................... 26

Vessel Detection Performance – Narrow ............................................................................................... 27

[Req 205.1] Vessel Detection Parameters .......................................................................................... 27

[Req 205.2] Tactical User or Theatre Ordering Capability .................................................................. 28

[Req 205.3] Atmospheric Conditions .................................................................................................. 28

Tactical Ordering & Reception ................................................................................................................ 29

[Req 206.1] Receiving Tactical Orders in Theatre ............................................................................... 29

[Req 206.2] Access the SBS Asset via Line-of-sight Communications ................................................. 29

[Req 206.3] Encryption of the Up/Downlink ....................................................................................... 29

Vessel Classification Performance .......................................................................................................... 30

[Req 207.1] SAR Properties & Imagery ............................................................................................... 30

[Req 207.2] Velocity Detection ........................................................................................................... 30

[Req 207.3] Ship Wake Analysis .......................................................................................................... 30

[Req 207.4] Vessel Characteristics ...................................................................................................... 30

[Req 207.5] Behavioural Information ................................................................................................. 31

Common Maritime Transmissions (CMTs) .............................................................................................. 32

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viii UNCLASSIFIED DND SBS DRD - V1.0 - 28 February 2017

[Req 208.1] Concurrent Detect & Geo-Locate CMTs .......................................................................... 32

[Req 208.2] AIS Detection Performance ............................................................................................. 32

[Req 208.3] AIS & CMT Geo-Location Accuracy .................................................................................. 33

[Req 208.4] CMT Coverage ................................................................................................................. 33

[Req 208.5] CMT Duty Cycle ............................................................................................................... 33

[Req 208.6] Security & Privacy of Canadians ...................................................................................... 33

[Req 208.7] Exploit Transmission Information ................................................................................... 34

Association .............................................................................................................................................. 35

[Req 209.1] Association Latency ......................................................................................................... 35

Advanced Maritime Data Analysis .......................................................................................................... 36

[Req 210.1] Data Analysis Tools .......................................................................................................... 36

[Req 210.2] SBS System Ground-Based Components ......................................................................... 36

Land Surveillance ........................................................................................................................................ 37

Imagery – Domestic & Arctic .................................................................................................................. 37

[Req 300.1] Canada’s Land Mass & Arctic .......................................................................................... 37

[Req 300.2] Canadian Beam Modes & Resolutions ............................................................................ 37

[Req 300.3] Domestic & Arctic Imagery Transfer to Multiple Systems .............................................. 38

Imagery – Expeditionary ......................................................................................................................... 39

[Req 301.1] SAR Imagery of Land Regions .......................................................................................... 39

[Req 301.2] Expeditionary Imagery Transfer to Multiple Systems ..................................................... 39

Imaging Performance – Strategic ............................................................................................................ 40

[Req 302.1] Produce High-Resolution Imagery ................................................................................... 40

[Req 302.2] Change Detection ............................................................................................................ 40

[Req 302.3] Geo-Locating Accuracy .................................................................................................... 40

Imagery Performance – Tactical ............................................................................................................. 41

[Req 303.1] Produce High-Resolution Imagery ................................................................................... 41

[Req 303.2] Change Detection ............................................................................................................ 41

[Req 303.3] Geo-Locating Accuracy .................................................................................................... 41

Moving Target Indication – Land ............................................................................................................ 42

[Req 304.1] Real-time Motion of Objects ........................................................................................... 42

Spectral Analysis for Land Surveillance and Mapping ............................................................................ 43

[Req 305.1] EO & HSI Imagery ............................................................................................................ 43

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[Req 305.2] Low-Latency Cross-Cueing ............................................................................................... 43

[Req 305.3] Sharing ............................................................................................................................. 43

Requirements Matrix .................................................................................................................................. 44

Definitions ................................................................................................................................................... 48

List of Acronyms .......................................................................................................................................... 50

Annex A: North American & Arctic AOIs ..................................................................................................... A1

Annex B: DND’s Global Maritime Surveillance Zones ................................................................................. B1

Annex C: Classified Space-Based Surveillance Requirements ..................................................................... C1

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1/53 UNCLASSIFIED DND SBS DRD - V1.0 - 28 February 2017

Introduction This Department of National Defence/Canadian Armed Forces (DND/CAF) Space-Based Surveillance Requirements (SBS-R) Document formalizes the UNCLASSIFIED surveillance requirements for the design and development of future Space-Based Surveillance (SBS) systems that will be composed of space-based synthetic aperture radar (SAR), Automatic Identification System (AIS), Electro Optical/Infrared (EO/IR), Hyperspectral Imaging (HSI), Signals Intelligence (SIGINT) and other space-based surveillance payload capabilities. Classified requirements are referred to in various annexes, but not included.

Although this SBS-R document is heavily focussed on SAR, it is intended to capture all space-based surveillance requirements. The focus remains on SAR because it is derived from the RADARSAT Next Generation (RNG) Users Requirements Document (URD) as well as the URD from the original RADARSAT Constellation Mission, (RCM), scheduled for launch in 2018. In addition to this document having its origins in SAR, DND/CAF is filling an international niche role in surveillance from space; namely active-wide-area-surveillance (AWAS) of the maritime domain. This AWAS capability continues to provide significant benefit for queued reconnaissance for the Royal Canadian Navy (RCN) and Canada’s Allies.

The upcoming RCM will provide DND/CAF with a significant AWAS capability as the constellation will be composed of three small satellites that will be equally-spaced around a 12-day exact-repeat, dawn-dusk orbit. Each RCM satellite will carry a SAR and AIS payload. Exploitation of RCM for DND requirements, in particular the near-real time production of AIS-enhanced SAR ship detection reports, is being implemented through the Polar Epsilon 2 (PE2) Capital Project. RCM is a Government of Canada (GC) led project being implemented by the Canadian Space Agency (CSA).

Aim The SBS-R is a living document that will be updated regularly IAW developing requirements and technological advancements. Elements of these SBS requirements will inform continuing Research and Development (R&D) and is intended to impact designs implemented on future space-based surveillance missions. For example, the SBS-R will influence new Defence Research and Development Canada (DRDC) initiatives, including a current project entitled “Compress the Tasking, Collection, Processing, Exploitation, and Dissemination (TCPED) cycle for RCM follow-on missions.” The objective of this new initiative is to help define technologies and capabilities that could be implemented on future surveillance missions and/or as part of the ground segment exploitation systems to meet future needs and requirements of DND/CAF.

This DND/CAF SBS-R document will be widely disseminated to Other Governmental Departments (OGDs) within the GC, industry, and our Allies in order to capture and incorporate innovation, identify common requirements and synergies, and promote future partnerships in space-based surveillance.

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Governance The development and promulgation of this document is the responsibility of Director General Space (DG Space). All requirements listed in this SBS-R document are the responsibility of an assigned OPI. Management and oversight of all requirements in this SBS-R document remains the responsibility of the DG Space Director of Space Requirements (DSR).

The SBS-R document will be reviewed at least every two years by a SBS-R Working Group (WG), which will validate all currently existing requirements and introduce new requirements as necessary. The primary method for adding requirements to the SBS-R document is through the recognized Statement of Capability Deficiency (SOCD) process. The final output of this SBS-R WG will be a revised SBS-R document, to be approved by DG Space.

There are three ways requirements can be amended or considered for inclusion:

a. Via a Statement of Operational Capability Deficiency (SOCD): DND/CAF can raise an SOCD which, once accepted by the respective L1, will be considered for inclusion by DSR or by the appropriate OPI of the SBS-R WG;

b. Direct Liaison with SBS-R WG attendees: Any member of the SBS-R WG can bring up new requirements for SBS to be tabled for consideration at the next SBS-R review. Therefore new requirements to the SBS-R can be inserted by engaging with the appropriate SBS-R WG member; and

c. Direct Liaison with DG Space DSR: Any potential SBS requirement may be considered for inclusion through direct engagement with the approval authority and supporting staff in the DG Space Intelligence Surveillance and Reconnaissance (ISR) Section.

It is intended that the SBS-R WG be as inclusive yet efficient as possible. Current SBS-R stakeholders and contributing representatives include:

a. DG Space/DSR – Lead; b. CFINTCOM; c. RCN; d. CJOC; e. DLR; f. RCAF DAR; g. CANSOFCOM; h. DG Cyber; i. CBRNE; j. NORAD; and k. DRDC.

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Context The importance of Remote Sensing or Space-Based Surveillance (SBS) from space is critical to enabling DND/CAF operations. SBS from space utilizes the ‘ultimate high ground,’ providing wide access to anywhere on the globe. There are numerous applications that stem from SBS from space information, such as:

a. Intelligence Imagery Products including Intelligence Preparation of the Battlefield (IPB); b. Coherent Change Detection (CCD); c. Mapping & Digital Elevation Models (DEMs); d. Active Wide Area Surveillance (AWAS) Ship-Detection & Tracking; e. Disaster Management (floods, fires, earthquakes); f. Battle Damage Assessments (BDA); g. Tunnel Detection; h. Ice Monitoring; i. Pollution detection & Monitoring; j. Marine Wind modelling; k. Surface soil moisture and crop monitoring; l. Ocean features analysis; and m. R&D.

Remote Sensing is comprised of several different sensing technologies that can be divided into active and passive categories. Although DND/CAF’s expertise resides in AWAS for Maritime Domain Awareness, there is also significant experience in the Canadian Forces Joint Imagery Centre (CFJIC) to analyze passive sources of imagery in addition to SAR imagery. Below is a brief overview of some of the remote-sensing technologies that are of interest to DND/CAF for operations:

Synthetic Aperture Radar (SAR) – This is an active sensor, which works day or night, and can penetrate the atmosphere through cloud cover or fog. The SAR sensor can detect targets that have a large enough Radar Cross Section (RCS), regardless of time of day and atmospheric conditions. This is the premise behind AWAS, which in contrast to passive or compliant systems has the potential to provide ‘ground-truth.’ This type of space-based sensor requires a significant power source, so these types of payloads are more complex and more costly, but provide a unique active-sensor capability.

Electro-Optical & Infrared (EO/IR) – The most common passive sensors, EO sensors typically gather Electromagnetic (EM) Radiation emitted or reflected by an object or surrounding areas on the surface of the Earth. Reflected sunlight is the most common source of this EM radiation. IR sensors focus on the IR portion of the EM spectrum. While invisible to the naked eye, this portion of the electromagnetic spectrum is capable of detecting and distinguishing objects and producing imagery based on thermal differences. Both the EO and IR sensors are typically restricted by cloud cover and/or daylight.

Hyper-spectral Imagery (HSI) – HSI is a passive sensor that divides the EM spectrum into many contiguous bands in order to detect the energy at various wavelengths. While EO/IR primarily senses the optical or IR portions of the EM spectrum, an HSI sensor collects a wider portion of the EM spectrum. As a result, an HSI sensor may have poorer frequency resolution than an EO/IR sensor. Instead, HSI focusses on narrower frequency bands and provides better spectral resolution by displaying differences and characteristics across the EM spectrum. HSI produces vast amounts of data, but also

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has vast potential to detect many characteristics that are invisible to EO/IR alone. HSI imagery is also negatively impacted by cloud cover and darkness.

Signals Intelligence (SIGINT) – SIGINT, another passive remote-sensing capability, focuses primarily on the interception of signals being transmitted by man-made transmitters. One UNCLASSIFIED form of SIGINT is the collection of Very High Frequency (VHF) emissions from the Automatic Identification System (AIS) used by ships. The detection of these AIS transmissions from space depends on ships carrying the AIS system to be compliant with the rules of use, otherwise incorrect or no data is collected. While powerful in its own right, the combination of AIS data with AWAS using SAR data is synergistic. The fusion of these two data sources results in significant benefits to those responsible for Maritime Domain Awareness. There also exists the ability to detect Common Maritime Transmissions (CMTs), which may be used to detect and identify vessels.

Note: Multi-sensor co-location and the combination of some of the different types of sensors mentioned above on the same platform or as part of a coordinated constellation can result in significant synergies. Fusion between remote sensing payloads and techniques has shown immense operational benefits over single source analysis. It is imperative that solutions to the requirements laid out in this document consider, from the beginning, potential synergies between different payloads in order to provide benefit to the various DND/CAF operations and applications.

Layout This SBS-R document contains requirements for three aspects of Space-Based surveillance (SBS). The requirements have been organized by SBS capability (100-level requirements), maritime surveillance (200-level requirements), and land surveillance (300-level requirements). Each requirement is stated followed by sub-requirements and comments to provide context. Each requirement also specifies the DND Office of Primary Interest (OPI), and reference to any applicable SOCDs.

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Generic SBS Requirements

Operational SBS Capability OPI: Director of Space Requirements (DSR)

SOCD: 2016-003 – Arctic Maritime Surface and Sub-Surface Domain Awareness – 24 Oct 2016 – CJOC J3 [Req 100.1] Multi-Role Operational Surveillance Capability The SBS system is required to provide a multi-role operational surveillance capability over land, maritime and Arctic areas of interest (AOIs) to DND/CAF. Comments:

The SBS system contributes imagery and information to support land applications and intelligence products, including IPB by providing data to describe the operational environment including:

o Operational planning; o Mapping, which includes topography, terrain classification, shoreline, and littoral zone

bathymetry; and o Detection of targets and detection of change.

The SBS system contributes imagery and information to support maritime surveillance including: o Detection, classification, identification, and tracking of vessels, day and night, in all

weather conditions, and in near-real-time; and o Ocean features to define the state of the ocean and establish the impact on operational

sensors.

The SBS system contributes imagery and information to support year-round Arctic surveillance. Arctic surveillance has unique characteristics due to prolonged light or darkness and the complicating presence of ice and icebergs. Capabilities required include:

o Detection of targets and detection of change; o Mapping of facilities and infrastructure; o Environmental assessment and monitoring (including ice, icebergs, and ice-flows); o Operational planning/IPB.

The scope of the SBS system capabilities is essentially restricted to land and maritime surveillance applications; air surveillance is not considered to be within the scope of these requirements due to the challenges of conducting air surveillance from space. However, land surveillance of air interests remains valid (such as airports, runways, tarmacs, etc.) and is captured under land surveillance; and

The SBS system could comprise of SAR, AIS, EO/IR, HSI, SIGINT and other complementary sensors.

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[Req 100.2] Global Access

The SBS system must have global access to support domestic and expeditionary operations. Comment:

Global includes the North Pole, but the exclusion of 80° south of the Earth's equatorial plane and beyond is acceptable.

[Req 100.3] Efficient Data Processing, Exploitation, and Dissemination (PED) The SBS system is required to possess automated, semi-automated, and manual operations for PED.

Comment:

An efficient PED process is critical for timely and effective exploitation by an Imagery Analyst (IA).

[Req 100.4] Interoperability The SBS system is required to be interoperable with other national and 5-Eyes systems within a system-of-systems operational context to the maximum extent possible.

Comment:

Factors that affect interoperability include:

o Sovereign control of the SBS system; o Data sharing and Security Policies; and o Collection Priorities.

[Req 100.5] 24/7 Operability The SBS system is required to be capable of 24/7 operations.

[Req 100.6] RCM Continuity

The SBS system is required to provide continuity of the AWAS SAR capability beyond the RCM.

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Whole of Government Operations OPI: DSR

SOCD: N/A

[Req 101.1] Support for DND/CAF and other GC Operations The SBS system is required to be capable of Whole-of-Government (WoG) operations; supporting primarily DND/CAF and other GC operations, to the greatest extent possible. Comments:

In order to maximize investment by the GC, the SBS must support a WoG development and operational approach; and

A WoG approach may allow for a holistic long-term programmatic approach for a Canadian SAR capability.

[Req 101.2] WoG Interoperability The SBS system is required to have the ability to produce, handle, and archive information products across DND/CAF and GC.

Req [101.3] Compliance with GC Directives & Policies The SBS system is required to comply with GC directives and policies, including but not limited to:

Remote Sensing Space Systems Act (RSSSA);

Security and Data Policies; and

GC Open Data Directive.

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System-of-Systems Operations OPI: DSR

SOCD: N/A [Req 102.1] System-of-Systems Capability The SBS system is required to operate as an element within, and complementary to, a system-of-systems surveillance capability. Comments:

DND/CAF may have future sensors, including space-based, with which this SBS is required to support. For example, DND/CAF may use a low-resolution sensor to queue other higher-resolution sensors as part of a system-of-systems;

Allies have surveillance capabilities, to which this SBS system will operate and potentially contribute. This SBS system is required to operate as part of a system-of-systems, to the greatest extent possible;

The SBS system may be required to be tasked and exploited collaboratively with or in support of Allies. This will include low-latency collection requirements based on a pre-arranged priority schema; and

The fulfillment of this requirement will require liaison and outreach with Allies.

[Req 102.2] Ally System Tasking The SBS system is required to permit Canadian Allies to submit low-latency tasking and collection requests. Comment:

It is widely recognized that DND/CAF greatly benefits from access to US intelligence community space assets and information. DND/CAF also has the ability to submit tasking requests to their sensors. This requirement not only reciprocates, but also enables the system-of-systems concept with our Ally’s assets.

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Responsive Planning & Ordering OPI: DSR

SOCD: 2015-20 – Lack of Data Fusion Tools Within the Recognized Maritime Picture – RJOC(P) [Req 103.1] Real-Time Collection Planning The SBS system is required to receive, process, and affect changes to the collection plan of the space-based payload continuously in real-time. Comments:

This planning capability is essential to support military operations and GC tasks such as humanitarian missions, disaster relief, search and rescue operations, and small vessel surveillance;

Timelines to execute changes to the collection plan are traditionally limited by the locations of ground stations and the ordering systems, which usually take days or weeks, rendering them non-responsive to support some operations (e.g., anti-piracy operations against small, fast vessels). Flexibility to quickly adjust the collection-plan on a global scale is therefore necessary;

Changes to the system collection plan could be cued by other surveillance systems (including surface, airborne, and space-based); and

The balance between SBS sensor revisit versus low-latency ordering must factor into the operational application. For example, if the SBS sensor revisits a particular area once per day, then low-latency ordering will still be required if the AOI or target is moving and/or a narrow field-of-view is required.

[Req 103.2] Low-Latency Ordering The SBS system is required to be able to task the payload within half of one orbit globally. Comment:

Low-latency ordering is necessary to have the sensor focus on a particular narrow AOI, which will shift over time;

Cross-cueing will be an essential capability to enable this low-latency exploitation of the SBS sensor;

The fulfillment of this requirement would enable cross-cueing (i.e. receives and responds to tips to/from Canadian and Allied systems; provides cues to Canadian and Allied systems);

This may be achieved via access to a global network of ground stations (or communications) to upload collection requirements. Further, the ability to adjust the collection plan on the SBS payload within the visibility mask of the system (i.e., potentially in theatre) is required;

This is not specifically TTC, but rather is the ability to adjust the collection plan on the SBS payload in real-time (i.e., potentially in theatre); and

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This requirement implies that other mechanisms to upload commands to the spacecraft may be necessary (e.g., machine to machine commands exchanged via a supporting satellite communications link).

[Req 103.3] System Priority Schema with Feedback The SBS system is required to have an automated priority schema with feedback in the event of conflicting collection requirements.

[Req 103.4] Emergency Override Priority The SBS system is required to have a priority schema to enable the capability to override other orders for purposes of health and safety of the system, emergency responses, national security events, or other urgent operations.

Comments:

A mechanism is required to enable WoG use while ensuring DND/CAF priority access for operations; and

This sort of non-routine override capability exists in RADARSAT-2, RCM, and is required for future systems.

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Low-Latency PED OPI: DSR SOCD: N/A

[Req 104.1] Global Latency The SBS system is required to be capable of providing SBS system imagery, products, and derived information globally, within 15 minutes of the surface of the Earth being imaged/sensed. Comments:

This requirement is particularly important for Search and Rescue, urgent operations, terrorist tracking and global ship detection products;

Global downlink comes in several forms with potential trade-offs: o X-Band downlink has a high bandwidth, which would allow for significant processing on

the ground, but potential higher latency due to fewer localized ground stations. o S-Band downlink has a medium bandwidth, also allowing for processing on the ground,

but again, ground stations may be fixed. o UHF/VHF – small, portable and cheap, but very low bandwidth. This would not be

suitable for imagery, but can receive small amounts of data such as a ship detection report text file. This would provide an extremely effective low-latency solution globally for a ship-detection application.

Downlink scenarios could include: o Download to a traditional ground receiving station with data processing and onward

distribution to theatre; o Download to a transportable ground station in theatre including the Unclassified

Remote-sensing Situational Awareness (URSA) mobile ground stations developed under the Joint Space Support Project (JSSP) (or follow-on capability); and

o Non-traditional downlink (e.g., via UHF/VHF or other satellite communications link) of derived information products generated on the spacecraft including to operational systems such as RCN ships and potentially RCAF units & aircraft or Canadian Army (CA) units & vehicles.

This requirement may necessitate an onboard processing capability of radar imagery and other sensor information with subsequent processing to generate low data volume information products. Alternatively, the system could comprise of global downlink stations with sufficient bandwidth and processing capabilities in order to meet the latency requirement.

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Protection & Security Measures OPI: DSR

SOCD: N/A

[Req 105.1] Security & Protection Measures The SBS system is required to have sufficient security and protection measures in place to protect the SBS assets from potential hostile events or accidents. [Req 105.2] Unclassified & Classified Operations The SBS system is required to support unclassified and classified operations of the entire TCPED cycle, including archival.

Comment:

Security and protection mechanisms must be in accordance with the Security and Assessment Authority under the direction of the Director of Information Management Security (DIM SECUR).

[Req 105.3] Secure Up/Downlinks The SBS system is required to have secure up and downlinks in accordance with GC security policies and the RSSSA.

Comment:

The Communications Security Establishment (CSE) and Global Affairs Canada (GAC) will provide guidance for the exact means to be used to protect data links IAW the RSSSA.

[Req 105.4] Jamming, Blinding, or Interference The SBS sensor is required to have protective measures in place to protect sensitive remote sensing components from being damaged in the event of jamming, blinding, or interference.

[Req 105.5] Ground Infrastructure & Network Security The SBS system ground infrastructure and network connectivity is required to be protected IAW physical, network and cyber security policies.

[Req 105.6] SBS System Manoeuvrability Maneuverability requirements shall be identified for possible conjunctions based on payload type, satellite complexity, system redundancy, and asset value/operational impact.

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Data Sharing OPI: CFINTCOM SOCD: N/A

[Req 106.1] Data Product Sharing Any data generated by the SBS system is required to be shareable with DND/CAF defence partners, whether military or civilian, for operational defence and security purposes. Comments:

The SBS system data includes raw data, imagery (including complex imagery), and value added-products; and

Sharing requirements may extend beyond 5-Eyes, NATO, or other Canadian Allies as there may be DND/CAF operations where sharing with Non-Governmental Organizations (NGOs) is required. The intent is to ensure that operational commanders are not restricted in their ability to execute their missions. A request for permission to share SBS system data with another organization would increase the latency and complicates the execution of the mission.

[Req 106.2] Network Connectivity for Sharing The SBS system is required to have network connectivity with GC and Allied systems, including DWAN, GCNet, CSNI, Stone Ghost and Enhanced Imaging, and Reporting & Exploitation System (EIRES).

[Req 106.3] Data Format The SBS system data is required to be in a suitable format(s) to enable integration into other systems, including classified systems via their safe-guards.

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Networked Interface OPI: DSR

SOCD: 2016-003 – Arctic Maritime Surface and Sub-Surface Domain Awareness – 24 Oct 2016 – CJOC J3

[Req 107.1] Product Ordering & Delivery The SBS system is required to include a networked connection and interface to simplify product ordering and delivery. Comments:

The interface streamlines user interaction with the SBS system and access to derived information products in order to enhance ease of use for DND/CAF operations;

The interface facilitates automated task submission;

The interface facilitates delivery of exploitation ready products (ERP) within operational timelines; and

ERP could include various types of change products, deformation maps, target detection reports, target velocity reports, AIS-enhanced ship detection reports, etc.

[Req 107.2] Network Bandwidth The SBS system bandwidth is required to be large enough to allow for timely access (<5 minutes) and retrieval of data from the archive and allow for the full exploitation of all data generated by the SBS system.

[Req 107.3] Cross-Domain Connectivity The SBS system interface is required to be capable of real-time cross-domain connectivity (i.e., UNCLAS but allowing transfer to higher level systems for SECRET and TOP SECRET exploitation). Comment:

Transfer from high to low level systems shall be done IAW DND security policies and likely require non-real time air gap.

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Archive OPI: CFINTCOM (advised by COS Dev, CFJIC, D Int IM)

SOCD: 2015-20 – Lack of Data Fusion Tools Within the Recognized Maritime Picture – RJOC(P); and

2016-003 – Arctic Maritime Surface and Sub-Surface Domain Awareness – 24 Oct 2016 – CJOC J3 [Req 108.1] Data Archive The SBS system is required to include an archive for all data generated by the SBS sensors as well as derived information products, which can be exploited en-masse by DND/CAF and GC. Comments:

Ready access to data and information products through a geospatial data repository (i.e., data archive) will support higher-level processing and analysis, near-real time operations, data fusion, and system interoperability; and

The SBS system archive of data for OGD support requires liaison and coordination with the department of Natural Resources Canada (NRCan), the Canada Centre for Mapping and Earth Observation (CCMEO) and potential integration into the National Earth Observation Data Framework (NEODF) and its follow-on, the Earth Observation Data Management System (EODMS).

[Req 108.2] Accessibility of Data Archive

The SBS system data archive is required to be easily accessible to all DND/CAF and OGDs.

[Req 108.3] Data Plan The archive is required to be searchable, and large volumes of data easily manipulated, so that the utility of the SBS system data repository can be maximized for future use.

[Req 108.4] Data Archive Connectivity The SBS system data archive is required to be connected to GC networks to support OGD remote-sensing operations to the greatest extent possible.

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[Req 108.5] Big Data Exploitation The SBS system data archive is required to be able to receive and exploit data from other systems and archives. Comment:

This requirement includes the ability to search for data and information products within operational timelines using simple tools and interfaces, which can process and analyze large volumes of data.

[Req 108.6] System Enduring Capability

The SBS system is required to have an enduring capability beyond the life of the space asset.

Comment:

Although the space component may no longer be operational, there will remain a need to exploit archived data and use existing networks.

[Req 108.7] Archive Life

The archived data is required to be accessible and exploitable until a point in time that there remains no

further relevance or operational utility to the archival data.

UNCLASSIFIED


Hybrid Classification Operation OPI: DSR

SOCD: N/A

[Req 109.1] System Security Classification The SBS system is required to be capable of operating at two levels of security (Level 1 – UNCLASSIFIED, and Level 2 – SECRET) with no added latency, throughout the TCPED cycle. Comments:

Enables operation at UNCLASS and Classified levels of security in order to allow for free and easy access and sharing of the SBS data; and

This may take the form of ad-hoc collection requirements and long-term systematic collects of sensitive areas both domestic and global.

[Req 109.2] Restricted Visibility The SBS system is required to have the ability to restrict visibility into the data ordering system and the geospatial data archive. Comment:

This requirement is intended to prevent the ability to deduce where classified collections may be required.

UNCLASSIFIED


System Life OPI: DSR

SOCD: N/A [Req 110.1] Space Mission Life

The space mission is required to have a minimum mission life of 15 years, and upon reaching end-of-life, must be capable of controlled re-entry, or re-entering naturally within 25 years.

Comments:

The minimum length of the SBS system mission life is based on the average procurement duration within the GC and DND/CAF. It is possible to have shorter procurement cycles, however, mission life must be sufficient such that no gap in AWAS coverage is assured;

Note that this mission life can be achieved through either one asset having a design life of 15 years, or a combination of assets launched over time to provide a capability over 15 years. The latter is preferable in order to reduce launch risks, increase resilience and redundancy to the capability, while providing a potential avenue for technical improvements;

A programmatic approach for a governmental SBS capability is preferred over implementing new procurement projects every 15 years;

Controlled re-entry or natural re-entry is based on the space-debris mitigation guidelines established by the United Nations;

IAW guideline 6 of the United Nations Office of Outer Space Affairs (UNOOSA) and the “Space Debris Mitigation Guidelines of the Committee on the Peaceful Use of Outer Space” the SBS systems long-term presence in a low-earth-orbit (LEO) must be limited;1 and

The Inter-Agency Space Debris Coordination Committee (IADC) has found 25 years to be a reasonable and appropriate lifetime limit.2

[Req 110.2] Ground Segment Life

The ground segment is required to be designed to have a life that well exceeds the mission life, including

ground stations and networks.

Space-Based Surveillance System Training

1 United Nations Office of Outer Space Affairs, Space Debris Mitigation Guidelines of the Committee on the Peaceful Uses of Outer Space http://www.unoosa.org/pdf/publications/st_space_49E.pdf. 2 Inter-Agency Space Debris Coordination Committee, Space Debris Mitigation Guidelines, http://www.unoosa.org/documents/pdf/spacelaw/sd/IADC-2002-01-IADC-Space_Debris-Guidelines-Revision1.pdf.

http://www.unoosa.org/pdf/publications/st_space_49E.pdf

http://www.unoosa.org/documents/pdf/spacelaw/sd/IADC-2002-01-IADC-Space_Debris-Guidelines-Revision1.pdf

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OPI: DSR SOCD: N/A

[Req 111.1] Overall Training Training is required to be provided to operational stakeholders, maintainers, and force developers across DND/CAF. Comment:

Training is required in order for all stakeholders to understand the potential, utility and applications associated with remote-sensing and SBS system capabilities.

[Req 111.2] Tailored Training Tailored training for all levels is required, including:

Operational Commanders high level understanding of the capability;

Exploitation operators understanding how to exploit the system;

Technicians and maintenance personnel understanding how to maintain and upgrade the system; and

Periodic training of any personnel after Full Operating Capability (FOC) is required to ensure knowledge and expertise continues through staff turnover.

UNCLASSIFIED


Maritime Surveillance

SAR Surveillance of North American & Arctic Maritime AOIs OPI: CJOC

SOCD: 2015-70 – Persistent Active Surveillance of the EEZ relates – 28 Jan 2016 – MSOC(E)/Trinity; and

2016-003 – Arctic Maritime Surface and Sub-Surface Domain Awareness – 24 Oct 2016 – CJOC J3

[Req 200.1] AWAS coverage of North American and Arctic AOIs The SBS system is required to provide AWAS coverage of North American and Arctic AOIs for the primary purpose of ship detection with revisit rates IAW Annex A. Comments:

See Annex A for the North American & Arctic Maritime AOIs and revisit rates;

Within Annex A there are shaded areas for the Canadian Northwest Passage and the Northern Sea Route, which is expected to have a significant increase in traffic in the coming years. As a result, these sea routes will require additional coverage and revisit rates as defined in Annex A;

DND/CAF contributes to GC requirements to detect and track all vessels in the Maritime approaches to Canada and the DND/CAF Area of Interest (AOI);

DND/CAF, specifically the RCN, is required to generate Maritime Domain Awareness (MDA) in its entirety for the North American and Arctic Maritime AOIs in support of CAF operational requirements. This covers more than just maritime ship detection and includes the production of radar imagery for oceanographic surveillance (e.g., wind, wave height, ocean current, temperature, biologic presence), hazards to shipping detection and tracking (e.g., sea ice, icebergs, pollution, debris fields etc.); and

Day/night and all weather surveillance dictate that a radar surveillance capability is required to support this mandate.

[Req 200.2] Ship/Ice Discrimination The SBS system is required to provide both automatic detection of ships that are ice-breaking and automatic ship/ice discrimination where icebergs are present – depending on the time of year and ice coverage for the Arctic AOIs. Comments:

The primary application for this requirement is Ship Detection and classification. This requires AWAS with a swath wide enough to have contiguous coverage at the equator with a high enough resolution to classify the detected vessels; and

The surveillance problem differs for various latitudes – and is challenging to predict for 2025 and beyond due to receding arctic ice. In fact, the Arctic Councils committee on Arctic Monitoring

UNCLASSIFIED


and Assessment Program predict that the Arctic could be ice free in summer by 2050.3 For now however, there are three ship detection applications (Ice Free, Icebergs Possible, Ice-breaking):

Ice Free - West Coast of Canada & East Coast, South of 45 degrees latitude. This region of the North American and Arctic AOIs requires a wide swath, high enough resolution, with sufficient sensitivity to classify a ship, but not necessarily to discriminate the ship from a false detection due to icebergs.

Icebergs Possible - Between 45 and 75 degrees latitude – the surveillance problem becomes a hybrid of ships in ice when frozen and ships amongst icebergs when navigable. This requires a beam mode in high enough resolution, with sufficient sensitivity, and polarizations to discriminate between ships and icebergs when navigable, and ship tracks in ice when icebreaking.

Ice-breaking - Above 75 degrees latitude – Although the Arctic ice recession accelerates, it is expected that from 2025 to 2050 there will still be solid ice for ice-breaking for most of the year above 75 degrees. At this latitude, the SBS system must be capable of detecting ships breaking through ice. This requires a beam mode in high enough resolution with sufficient sensitivity to either detect a broken ice track left by ships, or to detect the ship itself within the ice matrix.

3 AMAP, 2011. Snow, Water, Ice and Permafrost in the Arctic (SWIPA): Climate Change and the Cryosphere. Arctic Monitoring and Assessment

Programme (AMAP), Oslo, Norway. xii + 538 pp.http://www.amap.no/documents/doc/snow-water-ice-and-permafrost-in-the-arctic-swipa-

climate-change-and-the-cryosphere/743

UNCLASSIFIED


SAR Surveillance of DND’s Global Maritime Surveillance AOIs OPI: CJOC (Advised by DNI)

SOCD: 2015-70 – Persistent Active Surveillance of the EEZ relates – 28 Jan 2016 – MSOC(E)/Trinity; and


[Req 201.1] AWAS coverage of DND’s Global Maritime Surveillance AOIs The SBS system is required to provide AWAS coverage of DND’s Global Maritime Surveillance AOIs for the primary purpose of ship detection with revisit rates IAW Annex B. Comments:

See Annex B for DND`s Global Maritime Surveillance AOIs and revisit rates;

DND/CAF maintains operational interest in, and has obligations to, AOI’s that extend to international and other territorial waters;

AWAS coverage combined with automatic detection and identification of vessels is required several times per day in order to provide timely and accurate Maritime Domain Awareness (MDA) data for building the Recognized Maritime Picture (RMP);

[Req 201.2] Concurrent Surveillance

Concurrent AWAS vessel detection and Automatic Identification System (AIS) and/or any other common maritime transmission (CMT) message reception are required. Other maritime transmission systems for identification may include:

New AIS channels & protocols;

VHF Data Exchange System (VDES);4

Navigation Data (NAVDAT) in the 500kHz band;

Long Range Identification and Tracking (LRIT);

Maritime Radars; and

Any other EM transmission that could enable the identification of ships at sea. Comments:

The operating concept is similar to that of RCM:

o AIS data provides classification and identification for transmission compliant vessels; o AWAS is mostly used for detection, but could have an additional role in classification,

and potentially behaviour (e.g., polluting), depending on resolution; and

4 VHF Data Exchange System and Navigation Data system background brief. https://www.iho.int/mtg_docs/com_wg/CPRNW/WWNWS5/WWNWS5-3-5-2_Presentation_VDES.pdf

https://www.iho.int/mtg_docs/com_wg/CPRNW/WWNWS5/WWNWS5-3-5-2_Presentation_VDES.pdf

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o AWAS provides confirmation of AIS data and detection of non-cooperative targets.

It is desired that the SBS system have equally-spaced temporal coverage (e.g., 4 times daily suggests that a radar image must be taken roughly every 6 hours, and more importantly there are no long durations (>6 hours) where no revisit rate has occurred).

[Req 201.3] Contiguous SAR Swath Coverage

The SBS system is required to have contiguous SAR swath coverage (i.e., without gaps) at the equator, while maintaining wide area vessel detection performance.

[Req 201.4] Capacity for 50% Growth of Global AOIs The SBS system is required to have sufficient AWAS duty cycle capacity to allow for a minimum of 50% DND/CAF growth of the Global AOIs.

Comment:

Global surveillance zones may increase over time, and additional Global Maritime Surveillance AOIs will be generated as future SBS missions are considered; and

Duty cycle can be a function of expanded satellite duty cycle, or multiple small satellite capabilities in order to provide the effect/requirement.

UNCLASSIFIED


Global Maritime Static Facility AOIs OPI: CFINTCOM (advised by CFJIC & DN O&P)

SOCD: N/A

[Req 202.1] Static Maritime Facilities The SBS system is required to provide high-resolution imagery over static maritime military facilities twice daily with a coherent change detection (CCD) revisit capability of no more than 4 days. Comments:

DND/CAF often has intelligence requirements to understand the disposition of adversarial forces. This requirement helps determine force size & location, capability, readiness, order of battle, pattern of life etc.); and

The AOIs for these static facilities are classified.

[Req 202.2] Ship Detection Close to Land The SBS system is required to have semi-automatic ship detection close to land, in harbours and ports. Comments:

This requirement is for imaging and monitoring harbours and ports that an RCN ship may visit and other locations of strategic interest for Canada and DND/CAF; and

The reason for semi-automatic is to ensure an experienced Image Analyst (IA) can have the ability to check the results in the event of a high rate of false alarms due the proximity to land.

[Req 202.3] Rapid Beam Mode Switching The SBS system is required to have high resolution imagery close to regions where AWAS is also required, necessitating that the SBS system be capable of rapid switching from high resolution narrow swath to lower resolution wide swath.

UNCLASSIFIED


Naval Task Group (TG) Surveillance Zones OPI: DNR (advised by DN O&P)

SOCD: N/A [Req 203.1] Ship Detection of Naval TG Surveillance Zones The SBS system is required to provide AWAS coverage for the purpose of ship detection of Naval TG Surveillance Zones, providing 4 times daily revisit rates. This includes up to 5 separate AOIs located anywhere in the world that are centred on a moving point with a radius of 400 nautical miles.

Comments:

The ability to provide global SBS supports Canadian and Allied Naval Expeditionary operations;

This requirement is designed to provide an AWAS overhead/over-the-horizon view of the moving AOI surrounding a Naval TG(s);

Up to 5 AOIs is based on potential future deployment of individual RCN Ships plus Task Groups;

The 400 nm size of the AOIs is based on a time/speed/distance calculation for a ship moving at just over 30 knots over a 12-hour period. It is assumed that a 12-hour period is required due to polar LEO orbit SBS system providing twice-daily revisit at a minimum. Revisit period fluctuations may affect the 400 nm radius of these ad-hoc AOIs; and

Vessel detection performance ranges from large (> 25 m) to small (~5 m) for these TG AOIs.

[Req 203.2] Direct Satellite Tasking

The SBS system is required to be directly taskable by a ship within the TG either once it is within line of sight, or globally through communication links.

Comment:

The center point of the AOIs will continuously change, necessitating the requirement for direct taskings from a TG ship.

UNCLASSIFIED


Vessel Detection Performance – Wide Area OPI: CJOC

SOCD: 2016-003 – Arctic Maritime Surface and Sub-Surface Domain Awareness – 24 Oct 2016 – CJOC J3 [Req 204.1] Vessel Detection Parameters The SBS system is required to automatically detect vessels of 15 meters length and larger with a 90% probability of detection and a very low false alarm rate, in all weather conditions up to and including sea state 5 Beaufort Wind Scale 6 (wave height 3 to 4m), for all of the Maritime Areas defined in Annexes A and B.

Comments:

Predicted detection performance requires that ship and sea clutter models be defined, agreed to, and used to verify design compliance. False alarm reduction must include image artefacts like range and azimuth ambiguities;

The purpose of this requirement is to address large strategic AOIs with wide swath coverage for medium to large vessel detections; and

The required probability of false-alarm is less than 2.5(10)-9 over a resolution cell of 50 metres by 50 metres.

[Req 204.2] Atmospheric Conditions The SBS system is required to detect ships in all atmospheric conditions (day/night, cloud cover, fog etc.) with a low false alarm rate to ensure false contacts are not passed to Maritime authorities.

Comments:

Vessel detection performance must be maintained in the presence of sea ice and atmospheric moisture, such as rain, snow, cloud and fog; and

Predicted detection performance requires that ship and sea clutter models be defined, agreed to, and used to verify design compliance. False alarm and range ambiguity reduction must include image artifacts like range ambiguities.

[Req 204.3] Detection Performance in Ice The SBS system is required to have the same automatic ship detection performance as stated above, even for areas where icebergs are present and where ships may be breaking ice.

UNCLASSIFIED


Vessel Detection Performance – Narrow OPI: CJOC


[Req 205.1] Vessel Detection Parameters The SBS system radar is required to automatically detect vessels of 5 meters in length or larger with a 90% probability of detection, in all weather conditions up to an including sea-state 4 Beaufort Wind Scale 5 (wave height 1 to 2 m) for all Maritime AOIs except Arctic. Comments:

Predicted detection performance requires that ship and sea clutter models be defined, agreed to, and used to verify design compliance. False alarm reduction must include image artefacts like range and azimuth ambiguities;

This requirement applies to all AOIs, including the Naval TG Surveillance Zones;

The purpose of this requirement is to address smaller tactical AOIs with a narrower swath and higher resolution to detect smaller vessels or vessels with smaller radar cross section (RCS);

In certain regions, in order to support tactical and operational missions, such as support to SOF, counter-drug or counter-piracy, smaller vessels (Go-fasts, Pangos, semi-submersibles, or composite vessels) must be reliably detected in lesser sea states;

Go-fast and other small boats are difficult to detect, but their global proliferation and increased use by military and criminal actors alike requires enhanced detection abilities; and

Swath size for these regions must remain large enough to be of operational use since smaller vessels with lower sea states can travel faster and further. As such, the surveillance problem for smaller vessel detection must combine factors such as higher resolution, smaller swath, and low latency orders and automated real-time detection to be of use.

UNCLASSIFIED


[Req 205.2] Tactical User or Theatre Ordering Capability The SBS system is required to have a tactical user or theatre ordering capability via VHF and/or UHF to the space asset.

Comment:

The intent is to allow a Naval TG to be able to conduct over-the-horizon surveillance in real time in order to enable the potential interdiction of small vessels.

[Req 205.3] Atmospheric Conditions The SBS system is required to detect ships in all atmospheric conditions (day/night, cloud cover, fog etc.) with a low false alarm rate to ensure false contacts are not passed to Maritime authorities.

UNCLASSIFIED


Tactical Ordering & Reception

[Req 206.1] Receiving Tactical Orders in Theatre The SBS system is required to be capable of receiving low-latency tactical orders in theatre from ships or a naval TG for automatic ship detection and downlink in real-time. Comment:

While ships and TGs are deployed, the need to build the RMP around the TG is paramount, and is often limited by the Earth’s horizon with the ship’s own sensors. An overhead view of the AOI in real-time allows the TG to operationally respond to potential threats and maintain situational awareness.

[Req 206.2] Access the SBS Asset via Line-of-sight Communications The SBS system is required to provide a ship and/or TG Line-of-sight communications.

Comments:

Line-of-sight communications can take the form of VHF and/or UHF; and

The provision of real-time downlink automatic ship detection data necessitates on-board processing of SAR imagery, AIS and other maritime transmissions processing, and the association of these data into a low-bandwidth ship detection report for immediate consumption in a usable data format by the ships at sea.

[Req 206.3] Encryption of the Up/Downlink The SBS system is required to have encryption of the line-of-sight up and downlink.

UNCLASSIFIED


Vessel Classification Performance OPI: CJOC

SOCD: N/A

[Req 207.1] SAR Properties & Imagery The SBS system is required to exploit SAR properties and imagery to provide as much information as possible that could be used for vessel classification, and/or behaviour. Comments:

Un-identified vessels are of the utmost concern to maritime authorities; any information that could be used to localize, characterize, classify, and identify vessels and their behaviour is invaluable; and

Using SAR as the AWAS sensor, if used and exploited correctly, can provide additional information that would be useful for maritime authorities.

[Req 207.2] Velocity Detection The SBS system is required to detect and estimate vessel velocity.

Comment:

Moving Target Indication (MTI) methods will be used to distinguish ships from icebergs.

[Req 207.3] Ship Wake Analysis

The SBS system is required to be capable of automatically conducting ship wake analysis and provide an estimate of the target course and speed with an accuracy of 5° and 2 knots respectively. Comment:

This requirement applies to both wide and narrow vessel detection performance.

[Req 207.4] Vessel Characteristics

The SBS system is required to exploit all aspects of the imagery to derive vessel characteristics with the

following occurrences:

a. Vessel or not and confidence level;

b. Length within 5 meters;

UNCLASSIFIED


c. Width within 3 meters;

d. Orientation/course within 5°;

e. Speed within 2 knots; and

f. Error estimates/confidence levels.

Comments:

Error estimates will give operational authorities a sense of how accurate or inaccurate the estimated vessel characteristics may be;

Ship/ice discrimination provides iceberg targets and contributes to vessel detection false alarm rate reduction;

Anomaly detection software will use SBS-derived data to aid operators in identifying a vessel’s alterations from planned course or erratic behaviour inconsistent with normal operations; and

At times, so little information may be present on a possible ship detection that virtually none of these additional circumstances can be determined. This may result in a low confidence that the detect is actually a ship and may be a false contact. This context is important to operational surveillance officers, as it may be the determining factor of whether another asset is cued to investigate or not.

[Req 207.5] Behavioural Information

The SBS system is required to identify and highlight vessel behavioural information such as:

a. Polluting/pumping bilges; and b. Jamming, blinding or attempting to deceive the SBS SAR sensor.

UNCLASSIFIED


Common Maritime Transmissions (CMTs) OPI: CJOC

SOCD: N/A

[Req 208.1] Concurrent Detect & Geo-Locate CMTs The SBS system is required to detect and geo-locate any CMT concurrently with the other SBS sensors. Comments:

Identification of unknown vessel detections provided by the SBS system requires significant effort on the part of DND/CAF surveillance officers. By exploiting the collection of AIS and other CMT data to the greatest extent possible, the critical task of identifying the vessels within the AWAS SAR swath is simplified;

Geo-location (no decryption or SIGINT capability required) of common maritime transmissions will enable operators to conduct ship/ice discrimination, some classification, and reduce false alarms from SAR;

Other maritime transmission systems for identification may include: o New AIS channels & protocols o VHF Data Exchange System (VDES);5 o Navigation Data (NAVDAT) in the 500 kHz band; o Long Range Identification and Tracking (LRIT); o Satellite Telephones; o Maritime Radars; and o Any other EM transmission, which could enable the identification of ships at sea.

[Req 208.2] AIS Detection Performance The SBS System is required to correctly process at least one AIS message (both class A & B) from a transmitting vessel with a minimum detection rate of 90% under the following conditions:

a. An absence of in-band and adjacent VHF interference; b. The vessel is within the instantaneous field of view for 5 minutes; and c. There are no more than 2,200 transmitting vessels within the instantaneous field of view.

Comment:

Class B AIS (recreational) is typically broadcast with lower power than Class A AIS; therefore, Class A usually dominates the performance of space-based AIS receivers. Nevertheless, it is a requirement that Class B AIS be received and correctly decoded.

5 https://www.iho.int/mtg_docs/com_wg/CPRNW/WWNWS5/WWNWS5-3-5-2_Presentation_VDES.pdf

https://www.iho.int/mtg_docs/com_wg/CPRNW/WWNWS5/WWNWS5-3-5-2_Presentation_VDES.pdf

UNCLASSIFIED


[Req 208.3] AIS & CMT Geo-Location Accuracy

The SBS system is required to independently geo-locate CMTs broadcasted by vessels to the greatest

extent possible.

Comments:

This requirement is deliberately left vague in terms of accuracy as it is currently unknown what is technically feasible for the various CMTs;

It is known that some satellite phones can be easily geo-located within meters of accuracy; and

It is assumed that each type of CMT will be unique and require different methods of exploitation in order to accurately geo-locate it.

[Req 208.4] CMT Coverage

The SBS system is required to be capable of detecting and geo-locating CMTs globally. Comment:

The DND/CAF requires a global collection of the AIS (Class A & B) data along with other CMTs. This data provides invaluable historical and contextual information for intelligence personnel, maritime authorities, DND/CAF surveillance officers, and its Maritime Security Operation Centre (MSOC) partners.

[Req 208.5] CMT Duty Cycle

The SBS system is required to have Near-Continuous Radio-Frequency (RF) receiver operation in order to facilitate global MDA. Comment:

This requirement is designed to prevent the restriction of or limit the RF receiver duty cycle.

[Req 208.6] Security & Privacy of Canadians

The SBS system is required to be capable of filtering and/or automatically censoring information (also known as minimization) on Canadians to DND/CAF authorities.

Comment:

In collecting CMT globally, measures must be put in place to ensure the security and privacy of Canadians IAW the National Defence Act (NDA) s. 273.65(2)(d).6

6 National Defence Act. http://laws-lois.justice.gc.ca/PDF/N-5.pdf.

http://laws-lois.justice.gc.ca/PDF/N-5.pdf

UNCLASSIFIED


[Req 208.7] Exploit Transmission Information

The SBS system is required to exploit transmission information in order to verify the compliancy of AIS and other CMTs. Comments:

AIS signal spoofing limits the value of AIS-enhanced SAR ship detection reports. Other CMTs may experience the same issues and warrant a method of compliancy verification or spoofing detection;

AIS spoofing has been detected by tracking the behaviour of specific targets over time and/or by carrying out Doppler and timing analysis of the collection of received AIS messages. This method can be used on other transmissions as well;

Non-compliancy detection extends to both dynamic and static AIS data; and

Compliancy verification examples include: o Geo-location comparison to reported positions; o SAR detected course and speed to reported course and speed; and o Cross-referencing between all AIS and CMT sources.

UNCLASSIFIED


Association OPI: CJOC

SOCD: N/A

[Req 209.1] Association Latency The SBS system is required to provide SAR ship detection and identification using the associated AIS and/or the other CMTs as stipulated below:

Within a domestic ground-station mask – less than 5 minutes;

Globally – less than 30 minutes; and

A tactical user or Naval TG – less than 5 minutes.

Comments:

The timing starts from SAR illumination of a vessel to the dissemination of a ship-detection report;

The lower the latency in disseminating ship detection reports, the more valuable the data becomes to DND/CAF operators, surveillance officers as well as other maritime authorities and allies; and

Onboard processing using advanced RF signal processing algorithms may be required in order to achieve the above latency requirements.

UNCLASSIFIED


Advanced Maritime Data Analysis OPI: CJOC

SOCD: 2015-20 – Lack of Data Fusion Tools Within the Recognized Maritime Picture – RJOC(P); and


[Req 210.1] Data Analysis Tools The SBS system is required to include data analysis tools to enable and simplify processing of the vast amount of information gathered by the surveillance assets. Comment:

DND/CAF Surveillance officers, sensor operators and analysts may experience ‘big-data’ overload with various sensor data inputs, but limited resources to best exploit or ‘make sense’ of the data.

[Req 210.2] SBS System Ground-Based Components The SBS system ground-based components are required to include advanced processing to exploit other parameters of the data collected, to include as a minimum:

Track history;

Anomaly detection (unexplained speed and course changes, blue-water rendezvous, loitering, skirting EEZ etc.);

Big data manipulation & product reports (data filtering, searching, historical reports, vessel history reports, and other trend reporting mechanisms including coverage reports); and

Predictive analytics is an emerging capability that will provide enhanced capability to DND/CAF.

UNCLASSIFIED


Land Surveillance

Imagery – Domestic & Arctic OPI: CFINTCOM (advised by CJOC, MCE & CFJIC)


[Req 300.1] Canada’s Land Mass & Arctic The SBS system is required to provide imagery of Canada’s land mass and Arctic region.

Comments:

See Annex A for a map of the Canadian land mass and Arctic region zones;

Space-based SAR is one of the most suitable remote sensing systems for surveillance of Canada’s North. Due to the remoteness, harsh environment and extended darkness for much of the year, SAR imagery is required for Canada’s Domestic land regions and the Arctic;

DND and its OGD partners are mandated with the defence and security of Canada, which includes search and rescue operations;

Use of SAR for imaging land in coastal areas can provide data to improve shoreline data, which would benefit Hydrographic Services Office (HSO) and RCN;

Imagery based on the visible spectrum is the most intuitive type of imagery to exploit, can capture details not detectable via SAR, and is a necessary compliment to SAR imagery; and

IR-based imagery can be used to identify the relative temperature of bodies for several purposes such as detection, identification and tracking.

[Req 300.2] Canadian Beam Modes & Resolutions The SBS system is required to have several different beam modes and resolutions for imagery of Canada’s AOIs, including:

High-resolution (≥ 1 m, ground-range by azimuth) narrow swath for high-resolution imagery applications including search and rescue; and

Medium-resolution (≥ 15 m, ground-range by azimuth) wide swath to provide imagery of larger areas (e.g., flood mapping, natural disaster assistance).

Comments:

There are no DND/CAF requirements for imagery with resolution courser than 50 m;

Visible, IR, and HSI surveillance is highly complementary to SAR (weather and time of year dependant for the Arctic);

Commercial imagery can be complimentary to government-owned sensors; and

EO and/HSI surveillance systems that observe outside the visible EM spectrum is useful for domestic operations, including support of OGDs.

UNCLASSIFIED


[Req 300.3] Domestic & Arctic Imagery Transfer to Multiple Systems

The SBS system is required to be capable of automatic transfer of domestic and Arctic imagery to operational systems at all three levels of security (UNCLASS, SECRET, and TOP SECRET).

UNCLASSIFIED


Imagery – Expeditionary OPI: CFINTCOM (advised by MCE & CFJIC) SOCD: N/A

[Req 301.1] SAR Imagery of Land Regions The SBS system is required to provide SAR imagery of land regions globally. Comments:

Global includes the North Pole, but the exclusion of 80° south of the Earth's equatorial plane and beyond is acceptable;

DND has operational interest in land regions outside of Canada in support of military operations such as generating intelligence products, IPB, humanitarian relief, disaster relief, and search and rescue;

EO, IR, HSI, and commercial imagery are highly complementary to one another as well as to SAR imagery;

Imagery for DND/CAF expeditionary operations requires different beam modes and resolutions. High-resolution (~1 m, ground-range by azimuth) narrow swath for high-resolution imagery and intelligence applications to medium-resolution ( ~15 m, ground-range by azimuth) wide swath to provide imagery of larger areas (e.g., Digital Elevation Models, mapping, etc.);

There are no DND/CAF requirements for imagery with resolution courser than 50 m;

SBS technology has expeditionary surveillance applications which can aid in the defeat of camouflage, detection of explosives, tunnel detection and/or other intelligence products; and

All land AOIs are CLASSIFIED.

[Req 301.2] Expeditionary Imagery Transfer to Multiple Systems

The SBS system is required to be capable of automatically transferring expeditionary imagery to operational systems at all three levels of security (UNCLASS, SECRET, and TOP SECRET).

UNCLASSIFIED


Imaging Performance – Strategic OPI: CFINTCOM (advised by MCE & CFJIC) SOCD: N/A

[Req 302.1] Produce High-Resolution Imagery The SBS system is required to produce high-resolution imagery (finer than 5 m, ground-range by azimuth) of up to 50 AOIs of dimensions up to 125 km x 125 km daily, plus up to 50 other AOIs of similar dimension twice a week. Additional ad-hoc requirements based on geo-political events will require additional high-resolution imaging capacity that does not prevent the aforementioned daily and weekly collection requirements. Comment:

DND/CAF is required to monitor numerous areas of the globe for defence and security purposes in support of DND/CAF and Allied operations. These areas can and will change depending on events.

[Req 302.2] Change Detection The SBS system is required to include ERPs such as Amplitude Change Detection (ACD) and Coherent Change Detection (CCD) under appropriate conditions at a time step not greater than 4 days. Comments:

ERPs and change products can enable the following:

o Detect changes (e.g., vehicle tracks and other human activity); o Detect and measure surface deformation using techniques such as interferometry; and o Provide mapping detail in support of IPB including topography, shoreline delineation,

land cover, trafficability, etc.

[Req 302.3] Geo-Locating Accuracy

The geo-location accuracy of each pixel must be better than one resolution cell.

UNCLASSIFIED


Imagery Performance – Tactical OPI: CFINTCOM (advised by MCE & CFJIC)

SOCD: N/A

[Req 303.1] Produce High-Resolution Imagery The SBS system is required to produce high-resolution imagery (~1 m, ground-range by azimuth) to monitor up to 150 AOIs of dimensions up to 5 km x 5 km daily, and globally. Comments:

DND/CAF is required to monitor numerous areas of the globe for defence and security issues in support of DND/CAF and Allied operations;

Facilities of interest include buildings, bridges, camps, ports, airfields, and other objects/anomalies; and

The visible and IR portion of the EM spectrum plus SAR provide valuable information for the creation of topographic maps, MDA, infrastructure management/monitoring and support IPB and Joint Operational Planning Process (JOPP) for DND/CAF operations.

[Req 303.2] Change Detection

The SBS system is required to include ERPs such as Amplitude Change Detection (ACD) and Coherent Change Detection (CCD) under appropriate conditions at a time step not greater than 4 days. Comments:

ERPs and change products can enable the following:

o The imagery is required to detect changes (e.g. vehicle tracks and other human activity); o Detect and measure surface deformation using techniques such as interferometry; and o Provide mapping detail in support of IPB including topography, shoreline delineation,

land cover, trafficability, etc.

[Req 303.3] Geo-Locating Accuracy

The geo-location accuracy of each pixel must be better than one resolution cell.

UNCLASSIFIED


Moving Target Indication – Land OPI: CFINTCOM (advised by MCE & CFJIC)

SOCD: N/A

[Req 304.1] Real-time Motion of Objects The SBS system is required to detect real-time motion of objects as small as a vehicle (~3m) at velocities as low as 2.8 m/s (10 km/hr). Comment:

It is recognized that traditional MTI is conducted using SAR, while new technologies with persistent sensors or video imaging can also detect movement of targets of interest. That said, reliable day and night collection not hardened by cloud cover necessitates an MTI capability using a SAR sensor;

Target velocity could be estimated using SAR to exploit target motion Doppler via MTI modes that employ several receive apertures;

Multi-aperture techniques can be used to improve the motion sensitivity thus increasing the probability of detecting small moving targets in the imagery. Without MTI, those small targets may not be detected by an image analyst;

It is understood that the high resolution of land imagery to exploit Doppler favours an X-band SAR payload; and

It is assumed that it is more challenging to detect vehicles at lower speeds. Thus, if motion as slow as 2.8 m/s is detectable, then all higher velocities of land vehicles will also be detectable.

UNCLASSIFIED


Spectral Analysis for Land Surveillance and Mapping OPI: CFINTCOM (advised by MCE & CFJIC)

SOCD: N/A

[Req 305.1] EO & HSI Imagery The SBS system is required to provide electro-optical (EO) and HSI imagery for land surveillance and mapping with resolutions ~0.5 m. Comments:

Spectral analysis has demonstrated utility for DND/CAF operations;

EO/IR and HSI imagery are complementary to SAR imagery for land applications;

Electro optical, but more specifically, Multi-Spectral Imaging (MSI) and Hyperspectral Imaging (HSI) sensors can be used to categorize spectrally-detected objects in an image; and

It is recognized that there is a trade-off between physical and spectral resolutions, and that the compromise between the two will be determined through specific mission requirements, applications and SME advice.

[Req 305.2] Low-Latency Cross-Cueing The SBS system is required to provide low-latency cross-cueing between the spectral imager and other sensors in order to aid in the classification and the analysis of targets of interest, including the classification of detected objects.

[Req 305.3] Sharing Any EO and HSI imagery, and data generated by the SBS system is required to be shareable with OGDs, and DND/CAF defence partners, whether military or civilian, for operational defence and security purposes. Comment:

Sharing requirements may extend beyond 5-Eyes, NATO, or other Canadian Allies as there may be DND/CAF operations where sharing with NGOs is required. The intent is to ensure that Operational Commanders are not restricted in their ability to execute their missions. By having to request permission to share SBS system data with another organization increases latency and complicates the execution of the mission.

UNCLASSIFIED


DND/CAF Requirements Matrix DND/CAF Priority Description

Priority 1 (Essential): if not achieved, mission failure or project will not be approved by DND or GC Priority 2 (Critical): if not achieved, severe mission and/or operational impacts, or unlikely project to be approved by DND or GC Priority 3 (Important): if not achieved, significant mission and/or operational impacts, project potentially approved by DND/GC Priority 4 (Desirable): if not achieved, minor mission and/or operational impacts, project approval not affected, transformational Priority 5 (Optional): if not achieved, no or insignificant mission and/or operational impacts, project approval not affected, transformational CSA Priority Description

Priority 1 (Operational): currently operational need for which the absence of data will directly impact the department capacity to deliver its mandate in 2025 Priority 2 (Pre-operational): need that is expected to become operational and improve the department capacity to deliver its mandate in 2025 Priority 3 (Emerging): need that is emerging and for which there is uncertainty on its eventual operational use by the department to deliver its mandate

Note: the CSA priority description column represents DND/CAF priorities using CSA’s word description of priorities.

Req # Title DND/CAF

Priority Description CSA

Priority Description Notes

System Requirements

100.1 Multi-Role Operational Surveillance Capability 1 1

100.2 Global Access 1 1

100.3 Efficient Data PED 3 2

100.4 Interoperability 3 2

100.5 24/7 Operability 2 1

100.6 RCM Continuity 3 1

101.1 Support for DND/CAF and other GC Operations 1 1

101.2 WoG Interoperability 2 1

101.3 Compliance with GC Directives & Policies 1 1

102.1 System-of-System Capability 2 2

UNCLASSIFIED


Req # Title DND/CAF



102.2 Ally System Tasking 2 2

103.1 Real-Time Collection Planning 2 2

103.2 Low-Latency Ordering 3 2

103.3 System Priority Schema with Feedback 3 1

103.4 Emergency Override Priority 1 1

104.1 Global Latency 2 1

105.1 Security & Protection Measures 1 1

105.2 Unclassified & Classified Operations 2 1

105.3 Secure Up/Downlinks 1 1

105.4 Jamming, Blinding, or Interference 1 2

105.5 Ground Infrastructure & Network Security 1 1

105.6 SBS System Manoeuvrability 1 1

106.1 Data Product Sharing 1 1

106.2 Network Connectivity for Sharing 1 2

106.3 Data Format 2 2

107.1 Product Ordering & Delivery 3 1

107.2 Network Bandwidth 3 1

107.3 Cross-Domain Connectivity 2 1

108.1 Data Archive 3 1

108.2 Accessibility of Data Archive 3 1

108.3 Data Plan 4 1

108.4 Data Archive Connectivity 2 1

108.5 Big Data Exploitation 3 3

108.6 System Enduring Capability 3 1

109.1 System Security Classification 2 1

109.2 Restricted Visibility 2 1

110.1 Space Mission Life 2 1

110.2 Ground Segment Life 2 1

110.3 Archive Life 3 1

111.1 Overall Training 2 1

UNCLASSIFIED


Req # Title DND/CAF



111.2 Tailored Training 2 1

Maritime Requirements

200.1 AWAS coverage of North American and Arctic AOIs 1 1 The coverage required for four times daily of the AOI is likely the most significant cost driver (i.e. determines number of spacecraft)

200.2 Ship/Ice Discrimination 2 2

201.1 AWAS coverage of DND’s Global Maritime Surveillance AOIs

2 1

201.2 Concurrent Surveillance 2 2

201.3 Contiguous SAR Swath Coverage 2 2

201.4 Capacity for 50% Growth of Global AOIs 4 3

202.1 Static Maritime Facilities 3 2

202.2 Ship Detection Close to Land 3 3

202.3 Rapid Beam Mode Switching 3 3

203.1 Ship Detection of Naval TG Surveillance Zones 3 3

203.2 Direct Satellite Tasking 3 3

204.1 Vessel Detection Parameters (Wide Area) 2 1

204.2 Atmospheric Conditions (Wide Area) 2 1

204.3 Detection Performance in Ice (Wide Area) 2 1

205.1 Vessel Detection Parameters (Narrow) 2 2

205.2 Tactical User or Theatre Ordering Capability (Narrow)

3 2

205.3 Atmospheric Conditions (Narrow) 2 2

206.1 Receiving Tactical Orders in Theatre 4 3

206.2 Access the SBS Asset via Line-of-sight Communications

4 3

206.3 Encryption of the Up/Downlink 4 3

207.1 SAR Properties & Imagery 2 2

207.2 Velocity Detection 2 2

207.3 Ship Wake Analysis 3 1

UNCLASSIFIED


Req # Title DND/CAF



207.4 Vessel Characteristics 2 1

207.5 Behavioural Information 3 2

208.1 Concurrent Detect & Geo-Locate CMTs 1 3

208.2 AIS Detection Performance 1 1

208.3 AIS & CMT Geo-Location Accuracy 2 3

208.4 CMT Coverage 2 3

208.5 CMT Duty Cycle 2 2

208.6 Security & Privacy of Canadians 1 1

208.7 Exploit Transmission Information 2 2

209.1 Association Latency 2 1

210.1 Data Analysis Tools 3 3

210.2 SBS System Ground-Based Components 3 2

Land Requirements

300.1 Canada’s Land Mass & Arctic 1 1

300.2 Canadian Beam Modes & Resolutions 1 1

300.3 Domestic & Arctic Imagery Transfer to Multiple Systems

2 1

301.1 SAR Imagery of Land Regions 1 1

301.2 Expeditionary Imagery Transfer to Multiple Systems 2 1

302.1 Product High-Resolution Imagery (Strategic) 1 1

302.2 Change Detection (Strategic) 1 1

302.3 Geo-Locating Accuracy (Strategic) 1 1

303.1 Produce High-Resolution Imagery (Tactical) 1 1 The spatial resolution requirement of ~1m will be a significant cost driver

303.2 Change Detection (Tactical) 1 1

303.3 Geo-Locating Accuracy (Tactical) 1 1

304.1 Real-Time Motion of Objects 3 3

305.1 EO & HSI Imagery 2 1

305.2 Low-Latency Cross-Cueing 3 2

305.3 Sharing 1 1

UNCLASSIFIED


Definitions

Amplitude Change Detection

Amplitude Change Detection (ACD) identifies change in the amplitude of imagery by co-registering and combining two images acquired at different times. The images are often displayed in red (earlier image) and blue (later image) so that the mnemonic “blue is new, red has fled” can be used.

Automatic Identification System

An automatic, VHF-based tracking system used on ships and by vessel traffic services (VTS) for identifying and locating vessels by electronically exchanging data with other nearby ships, AIS base stations, and satellites. When satellites are used to detect AIS signatures then the term Satellite-AIS (S-AIS) is often used. AIS information supplements marine radar, which continues to be the primary method of collision avoidance for water transport.

Bathymetry Bathymetry is the underwater depth of lake or ocean floors. In other words, bathymetry is the underwater equivalent to hypsometry or topography.

Coherent Change Detection

Coherent Change Detection (CCD) identifies changes in both the amplitude and phase of complex SAR imagery using the sample coherence change statistic. CCD has the potential to detect very subtle scene changes, including changes in sub-resolution cell scattering structure at the scale of the radar wavelength that may be undetectable using ACD techniques.

Common Maritime Transmission

Any electro-magnetic transmission that could potentially be detected from space that is common-place in maritime traffic and could aid in the identification and classification of ship detections.

Conjunction A conjunction is a close approach between two orbiting objects. Conjunction analysis is the art of combining measurements of the location and uncertainties in location of the objects and in propagating that information forward in time to the point of closest approach.7

Dark Target A maritime vessel that is detected via Synthetic Aperture Radar or EO/IR imagery, but has no detectable Radio Frequency emissions (e.g., AIS, VDES).

Derived Image Product

Derived Image Product or “DIP” means a product derived from Data or Data Products that still contain all or substantially all of the pixel structure and information of the original Data or Data Products. DIPs do not contain or retain phase information.

Hyperspectral Imaging

Hyperspectral Imaging (HSI) collects and processes information from across a wide portion of the electromagnetic spectrum. The goal of HSI is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes.

Image Collectively, the representations of objects reproduced electronically or by optical means on film, electronic display devices, or other media.

Interferometry The combination of two complex radar measurements of the same point to estimate the phase difference; these images may be taken at the same time from slightly different geometries, or at different times from similar geometries.

7 NASA Orbital Debris Program Office, Conjunction Description, http://orbitaldebris.jsc.nasa.gov/protect/collision_avoidance.html.

https://en.wikipedia.org/wiki/Vessel_traffic_service

https://en.wikipedia.org/wiki/Vessel_traffic_service

https://en.wikipedia.org/wiki/Marine_radar

http://orbitaldebris.jsc.nasa.gov/protect/collision_avoidance.html

UNCLASSIFIED


Intelligence The product resulting from the collection, processing, analysis, integration and interpretation of available information concerning foreign nations, hostile or potentially hostile forces or elements, or the geography and the culture that contributes to the understanding of an actual or potential operations environment.

Intelligence, Surveillance & Reconnaissance (ISR)

The activity that synchronizes and integrates the planning and operation of all collection capabilities, with exploitation and processing, to disseminate the resulting information to the right person, at the right time, and in the right format in direct support of current and future operations.

Maritime Domain Awareness

Maritime Domain Awareness (MDA) means having true and timely information about everything on, under, related to, adjacent to, or bordering a sea, ocean or other navigable waterway. This includes all related activities, infrastructure, people, cargo, vessels, or other means of transport. For marine security, it means being aware of anything in the marine domain that could threaten Canada's national security. (definition from the Interdepartmental Marine Security Working Group (IMSWG))

Moving Target Indication

A pulsed sensor signal that detects moving targets by using interferometry to measure changes in the phase of the returned signal.

Near-real time < 15 minutes

Non-compliant Vessel

A vessel that may or may not be a vessel of interest, but has provided evidence of activity that is not authorized by the IMO or other regulatory bodies (not transmitting on AIS, or did not report in via LRIT).

Product Information processed from data, for example by PE2, to meet an end-customer’s specifications, which in the SAR case could include:

Single Look Complex Data;

Data Products;

Derived Image Products; and

Value Added Products.

Real-time < 5 minutes

Synthetic Aperture Radar

A form of remote-sensing radar that is used to create images of the surface of the Earth. SAR uses the relative motion of the radar antenna over a targeted region to provide finer spatial resolution than is possible with conventional beam-scanning radars.

Surveillance The systematic observation of aerospace, surface or subsurface areas, places, persons, or things, by visual, aural, electronic, photographic, or other means.

Value Added Product Value Added Product (VAP) in the SAR context means any products that (a) are processed from Data using interferometric processing techniques, such as interferograms, coherent change detection products or interferometric digital elevation models; or (b) include a material addition of other external information, or have undergone significant enhancement, but do not retain the pixel structure of the original Data, Data Products or Derived Image Products. VAPs do not contain or retain phase information.

UNCLASSIFIED


List of Acronyms

ACD Amplitude Change Detection

AIS Automatic Identification System

AOI Area of Interest

AOR Area of Responsibility

AWAS Active Wide-Area-Surveillance

CA Canadian Army

CAF Canadian Armed Forces

CCD Coherent Change Detection

CCMEO Canada Centre for Mapping and Earth Observation

CEO Canadian Eyes Only

CFINTCOM Canadian Forces Intelligence Command

CFINTGP Canadian Forces Intelligence Group

CFJIC Canadian Forces Joint Imagery Centre

CJOC Canadian Joint Operations Command

CMT Common Maritime Transmissions

CSA Canadian Space Agency

CSE Communications Security Establishment

CSNI Consolidated Secret Network Infrastructure

CSPF Canadian Space Policy Framework

DEM Digital Elevation Model

DFATD Department of Foreign Affairs, Trade and Development (See Global Affairs Canada)

DG Director General

DIM Secur Director Information Management Security

DN O&P Director Naval Operations and Plans

DND Department of National Defence

DNI Director of Naval Intelligence

DoD Department of Defence (USA)

DRD DND Requirements Document

DRDC Defence Research and Development Canada

DSR Director of Space Requirements

DWAN Defence Wide Area Network

EEZ Economic Exclusion Zone

EIRES Enhanced Imaging, Reporting & Exploitation System

EM Electro-Magnetic

EMOC Enhanced Management of Orders and Conflicts

EO Electro Optical

EODMS Earth Observation Data Management System

ERP Exploitation Ready Products

FOC Final Operating Capability

GAC Global Affairs Canada

GC Government of Canada

GCNET Government of Canada Network

GEO Geo-stationary Earth Orbit

HSI Hyper Spectral Imaging

UNCLASSIFIED


HSO Hydrographic Services Office

IA Imagery Analyst

IADC Inter-Agency Space Debris Coordination Committee

IAW In Accordance With

IMSWG Interdepartmental Marine Security Working Group

IOC Initial Operating Capability

IPB Intelligence Preparation of the Battlefield

IR Infrared

ISR Intelligence, Surveillance & Reconnaissance

JOPP Joint Operational Planning Process

JSSP Joint Space Support Project

JTFN Joint Task Force North

L1 Level 1

LEO Low-Earth Orbit

LRIT Long Range Identification & Tracking

MCC Maritime Component Command

MCE Mapping and Charting Establishment

MDA Maritime Domain Awareness

MEO Medium-Earth Orbit

MSOC Marine Security Operation Centre

MTI Moving Target Indication

N/A Not Applicable

NAVDAT Navigation Data

NDA National Defence Act

NEODF National Earth Observation Data Framework

NGO Non-Governmental Organization

NORAD North American Aerospace Defense Command

NRCan Natural Resources Canada

OGD Other Government Department

OPI Office of Primary Interest

PE2 Polar Epsilon 2

PED Processing, Exploitation, Dissemination

R&D Research & Development

R2 RADARSAT-2

RCAF Royal Canadian Air Force

RCM RADARSAT Constellation Mission

RCN Royal Canadian Navy

RCS RADAR Cross Section

RF Radio-Frequency

RMP Recognized Maritime Picture

RNG RADARSAT Next Generation

RSSSA Remote Sensing Space Systems Act

SA&A Security Assessment & Authority

SAR Synthetic Aperture Radar

SBS Space-Based Surveillance

SBS-R SBS Requirements

UNCLASSIFIED


SCC Space Component Command

SIGINT Signals Intelligence

SOCD Statement of Operational Capability Deficiency

SOF Special Operations Forces

TCPED Tasking, Collection, Processing, Exploitation and Dissemination

TG Task Group

TT&C Tracking, Telemetry & Control

UNOOSA United Nations Office of Outer Space Affairs

URD User Requirements Document

URSA Unclassified Remote-sensing Situational Awareness

USA United States of America

VDES VHF Data Exchange System

VOI Vessel of Interest

WG Working Group

WoG Whole of Government

UNCLASSIFIED



UNCLASSIFIED

A1/6 UNCLASSIFIED DND SBS DRD - V1.0 - 28 February 2017

Annex A: North American & Arctic AOIs Canadian Domestic, Continental US and Arctic AOIs:

UNCLASSIFIED



UNCLASSIFIED


Zone Region Latitude Longitude Area (nm2) Requirement

JTF-A Canadian Atlantic 60:00:00:00N 064:09:35:44W

1,218,797 4 times daily (year round)

59:55:47:00N 045:28:04:00W

59:37:01:00N 043:51:22:00W

52:07:00:00N 031:52:00:00W

40:00:00:00N 046:00:00:00W

40:00:00:00N 066:00:00:00W

42:00:00:00N 066:00:00:00W

44:00:00:00N 067:00:00:00W

44:40:33:30N 067:00:15:00W

Follow international boundary

45:06:54:89N 067:06:51:95W

Follow NB/QC boundary

Follow QC coastline down Saint

Lawrence Seaway to Cornwall

45:00:25:12N 074:44:24:45W

45:00:33:92N 074:44:21:43W

Follow Saint Lawrence Seaway

and QC coastline

51:41:48:02N 057:10:86:10W

Follow QC/Labrador boundary

FOI-E Canadian Eastern 60:00:00:00N 064:09:35:44W

437,329 4 times daily (year round)

Follow NL/QC boundary

Follow QC coast line

Follow QC/ON boundary

Follow Hudson Bay coastline

JTF-C Canadian Central 56:51:06:60N 088:57:15:40W


Follow Hudson Bay coastline

Follow ON/QC border southeast


49:00:00:00N 095:09:54:37W

52:49:26:37N 095:09:41:50W

JTF-N Canadian North 89:59:59:96N 100:00:00.00W


83:09:58.92N 057:04:48.33W

Follow 12nm boundary along

Greenland coastline

59:55:47:00N 045:28:04:00W

60:00:00:00N 064:09:35:44W

Follow QC coastline

60:00:00:00N 095:00:00:00W

Follow 60°N parallel

60:00:00:00N 139:03:42:74W


60:18:21:52N 141:00:00:00W

UNCLASSIFIED


JTF-W Canadian West 60:00:00:00N 095:00:00:00W


Follow coastline

56:51:06:60N 088:57:15:40W

52:49:26:37N 095:09:41:50W

49:00:00:00N 095:09:54:37W

49:00:00:00N 114:00:00:00W

Follow BC/AB border

53:50:42:00N 120:00:00:00W

60:00:00:00N 120:00:00:00W

JTF-P Canadian Pacific 60:00:00:00N 120:00:00:00W


53:50:42:00N 120:00:00:00W

49:00:00:00N 114:00:00:00W

Follow international border

48:49:53:65N 124:78:69:63W

Follow 12nm boundary along US

coastline

44:49:12:00N 124:15:36:00W

36:42:00:00N 135:24:00:00W

48:18:00:00N 155:00:00:00W

56:10:12:00N 155:00:00:00W

Follow 12nm boundary along US

coastline

54:50:84:09N 133:42:42:47W

54:71:69:50N 130:61:00:33W


60:00:00:00N 139:03:42:74W

20 Bering Sea 58:49:48.00N 151:00:00.00W

550,226 Once daily (year round)

57:30:00.00N 151:30:00.00W

56:15:00.00N 153:49:48.00W

56:10:13.10N 154:59:45.03W

48:18:00.00N 155:00:00.00W

47:06:47.45N 151:46:48.37W

45:40:00.00N 152:37:00.00W

48:59:44.29N 179:58:54.14W

61:50:34.36N 166:15:03.42W

60:45:39.33N 165:06:49.15W

21 NW Pacific Artic 64:18:00.00N 161:28:00.00W

1,093,709 Once daily (year round) 45:13:00.00N 178:06:00.00E

44:47:00.00N 148:47:00.00E

67:26:00.00N 175:16:00.00W

22 Sea of Japan 61:56:00.00N 169:16:00.00E

953,261 Once every 3 days (year

round) 34:24:00.00N 142:33:00.00E

42:49:00.00N 132:00;00.00E

58:10:00.00N 137:54:00.00E

UNCLASSIFIED


23 E Siberian Sea 64:18:00.00N 161:28:00.00W

1,262,710

Once daily (year round)

69:50:11.12N 141:00:12.01W

77:43:29.04N 141:00:00.00W

75:59:00.00N 098:47:00.00E

68:31:00.00N 130:51:00.00E

24 Svalbard 70:39:00.00N 027:00:00.00E

523,098 Twice daily (year round) 81:36:00.00N 028:44:00.00W

76:25:00.00N 099:41:00.00E

25 S Barent Sea 77:43:28.78N 141:00:03.88W

489,432

Once daily (year round)

75:59:00.00N 098:47:00.00E

81:52:00.00N 028:38:00.00W

83:09:58.92N 057:04:48.33W

89:59:59:96N 100:00:00.00W

26 Norwegian Sea 71:05:00.00N 026:44:00.00E

634,303 Twice daily (year round) 62:05:00.00N 005:26:00.00E

69:14:00.00N 023:41:00.00W

81:50:00.00N 027:16:00.00W

27 W Greenland Sea 65:55:00.00N 006:48:00.00W

412,479 Once every 3 days (year

round) 69:51:00.00N 024:24:00.00W

60:24:00.00N 044:31:00.00W

56:19:00.00N 023:23:00.00W

A US Continental E 47:13:00.00N 037:54:00.00W


45:59:00.00N 036:13:00.00W

38:51:00.36N 044:34:00.59W

34:46:00.00N 054:33:00.00W

32:29:00.00N 063:56:00.00W

21:18:00.00N 070:17:00.00W

28:15:00.00N 078:35:00.00W

40:00:00.00N 065:55:23.81W

40:00:00.00N 046:00:00.00W

B US Continental W 41:45:57.44N 128:36:49.80W


36:41:59.99N 135:23:59.95W

27:53:19.00N 128:00:34.88W

18:00:00.00N 120:00:00.00W

21:24:41.75N 112:43:40.60W

32:11:02.57N 122:29:58.44W

C Barents Sea 75:59:00.00N 098:47:00.00E


62:54:00.00N 035:53:00.00E

71:05:00.00N 026:44:00.00E

D North Sea 55:31:09.27N 012:55:49.42E

278,192 Twice daily (year round)

50:59:31.47N 001:42:47.50E

58:31:39.36N 005:06:32.17W

65:54:58.57N 006:48:19.70W

62:05:40.85N 005:24:06.46E

UNCLASSIFIED


- Shipping Lane 1 74:56:13.6N 079:14:33.88W

822,578 (1 & 2

combined)

4 times daily during the Jul-Sep shipping season (Indicated by grey area)

74:58:11.18N 093:27:01.94W

75:15:42.95N 111:09:08.15W

76:20:22.42N 124:57:58.39W

73:06:03.55N 136:32:03.92W

70:10:52.91N 174:21:34.45W

72:00:00.00N 180:00:00.00W

74:50:47:95N 138:11:00.34E

78:19:51.33N 101:38:50.65E

76:19:37.80N 068:24:15.16E

70:33:28.84N 057:05:50.78E

72:43:44.63N 024:08:19.05E

63:07:41.88N 002:36:09.83E

62:02:13.07N 005:24:54.80E

70:35:52.36N 022:07:10.53E

69:15:54.21N 033:44:13.93E

68:39:20.19N 057:51:12.74E

77:42:43.46N 104:32:47.60E

72:40:58.82N 143:01:27.30E

69:02:31.38N 179:45:35.71E

66:04:35.49N 169:43:48.37W

62:20:58.03N 177:35:46.88W

60:27:51.10N 164:57:39.37W

68:18:40.17N 166:37:07.82W

70:06:44.87N 162:06:00.44W

68:46:33.86N 136:31:32.56W

70:08:03.22N 129:50:43.26W

67:38:55.01N 113:23:27.53W

67:41:13.80N 099:05:57.16W

68:46:27.25N 097:46:43.73W

73:44:21.68N 100:55:29.97W

73:31:48.91N 077:34:19.44W

- Shipping Lane 2 73:22:34.63N 105:37:17.05W

822,578 (1 & 2

combined)

4 times daily during the Jul-Sep shipping season (Indicated by grey area)

74:21:56.39N 124:28:20.25W

72:01:00.89N 125:32:59.33W

68:43:00.95N 113:32:38.35W

69:29:16.61N 102:26:57.01W

UNCLASSIFIED

B1/9 UNCLASSIFIED DND SBS DRD - V1.0 - 28 February 2017

Annex B: DND’s Global Maritime Surveillance Zones Central & South America AOIs:


1 S Caribbean 08:37:00.00N 077:08:00.00W


11:00:00.00N 075:29:00.00W

12:46:00.00N 071:30:00.00W

10:30:00.00N 064:29:00.00W

15:55:00.00N 062:20:00.00W

16:21:00.00N 073:06:00.00W

10:12:00.00N 079:09:00.00W

2 W Central America 15:40:00.00N 094:07:00.00W


01:56:00.00N 079:05:00.00W

13:40:00.00N 096:39:00.00W

3 W South America 01:40:00.00N 093:15:00.00W


02:12:00.00N 081:15:00.00W

02:20:00.00N 092:29:00.00W

4 E Central America 12:16:00.00N 083:29:00.00W


12:19:00.00N 081:17:00.00W

16:24:00.00N 082:00:00.00W

15:50:00.00N 084:14:00.00W

15:05:00.00N 083:11:00.00W

UNCLASSIFIED


Northern Europe AOIs:


5 TBP

6 TBP

7 TBP

8 E Baltic 60:05:00.00N 021:52:00.00E


60:34:00.00N 028:15:00.00E

59:56:00.00N 029:33:00.00E

59:26:00.00N 027:23:00.00E

59:14:00.00N 023:38:00.00E

9 S Baltic 57:50:00.00N 019:17:00.00E


54:24:00.00N 019:19:00.00E

55:39:00.00N 016:44:00.00E

UNCLASSIFIED


Southern Europe AOIs:


10 Black Sea 42:24:00.00N 027:27:00.00E


46:41:00.00N 031:03:00.00E

46:30:00.00N 037:56:00.00E

44:45:00.00N 036:57:00.00E

42:12:00.00N 041:35:00.00E

40:55:00.00N 040:03:00.00E

42:05:00.00N 033:28:00.00E

41:15:00.00N 031:07:00.00E

41:20:00.00N 029:05:00.00E

11 E Mediterranean 36:34:00.00N 035:47:00.00E


31:17:00.00N 029:48:00.00E

36:25:00.00N 028:20:00.00E

12 TBP

UNCLASSIFIED


Arabian Sea AOIs:


13 Persian Gulf 29:19:00.00N 048:14:00.00E


Strait of Hormuz 24:49:00.00N 050:46:00.00E

Gulf of Oman 23:45:00.00N 052:06:00.00E

24:06:00.00N 057:06:00.00E

21:03:00.00N 064:57:00.00E

23:22:00.00N 068:18:00.00E

25:15:00.00N 066:15:00.00E

26:29:00.00N 056:50:00.00E

27:11:00.00N 052:57:00.00E

29:48:00.00N 049:35:00.00E

14 TBP

15 TBP

16 W Arabian 16:22:00.00N 053:20:00.00E


20:32:00.00N 061:42:00.00E

14:44:00.00N 055:21:00.00E

17 Red Sea 27:51:00.00N 035:11:00.00E


14:12:00.00N 042:52:00.00E

13:43:00.00N 042:14:00.00E

15:46:00.00N 039:36:00.00E

26:50:00.00N 034:01:00.00E

18 Gulf of Aden 12:12:00.00N 043:36:00.00E 101,297 Twice daily (year round)

16:16:00.00N 053:18:00.00E

UNCLASSIFIED


14:43:00.00N 055:17:00.00E

11:56:00.00N 051:18:00.00E

10:38:00.00N 044:15:00.00E

19 Horn of Africa 02:52:00.00S 040:14:00.00E


08:20:00.00N 054:57:00.00E

04:30:00.00S 042:10:00.00E

UNCLASSIFIED


Pacific AOIs:


28 Yellow Sea 39:01:00.00N 117:28:00.00E


40:38:00.00N 121:17:00.00E

39:29:00.00N 125:17:00.00E

36:11:00.00N 126:59:00.00E

30:10:00.00N 121:54:00.00E

29 Sea of Japan 39:20:00.00N 127:29:00.00E


36:33:00.00N 137:30:00.00E

34:05:00.00N 130:44:00.00E

30 China Sea 22:55:00.00N 116:48:00.00E


33:43:00.00N 130:27:00.00E

06:56:00.00N 126:57:00.00E

31 Philippine Sea 33:47:00.00N 130:36:00.00E


06:30:00.00N 129:16:00.00E

07:09:00.00n 126:55:00.00E

32 W South China Sea 19:58:00.00N 105:16:00.00E


07:05:00.00N 116:59:00.00E

12:11:00.00N 109:25:00.00E

33 E South China Sea 22:55:00.00N 116:46:00.00E 237,513 Twice daily (year round)

09:08:00.00N 125:22:00.00E

UNCLASSIFIED


07:04:00.00N 117:01:00.00E

34 S South China Sea 08:25:00.00N 104:47:00.00E


12:07:00.00N 109:41:00.00E

07:06:00.00N 116:54:00.00E

00:41:00.00N 108:23:00.00E

02:10:00.00N 103:54:00.00E

35 E Strait of Malacca 02:44:00.00N 101:24:00.00E


04:48:00.00S 107:47:00.00E

02:16:00.00N 100:57:00.00E

36 W Strait of

Malacca 08:33:00.00N 097:59:00.00E


02:47:00.00N 101:21:00.00E

02:18:00.00N 100:49:00.00E

06:11:00.00N 095:42:00.00E

UNCLASSIFIED


West Africa AOIs:


37 West Africa 22:19:03.58N 026:57:00.25W


13:56:53.98N 027:20:24.89W

11:44:37.40N 016:43:32.51W

38 Gulf of Guinea 03:50:25.70N 007:14:12.73W


05:59:16.95N 004:02:55.12E

04:06:07.85N 005:43:36.30E

03:35:12.63N 008:55:16.32E

00:44:13.04S 008:13:30.47E

02:14:45.15S 004:18:37.81W

UNCLASSIFIED


Central Pacific AOI:


39 North Pacific 30:03:06.00N 134:56:07.00E

1,496,061 Twice daily (May-July)

29:59:14.00N 145:44:05.00E

30:03:12.00N 159:57:38.00E

30:14:25.00N 169:52:33.00E

29:57:04.00N 175:00:00.00E

47:58:58.00N 175:00:00.00E

48:58:34.00N 175:08:03.00E

49:51:07.00N 169:53:06.00E

51:23:25.00N 167:04:21.00E

51:58:35.00N 164:56:16.00E

50:13:37.00N 161:55:57.00E

48:38:10.00N 160:01:57.00E

45:06:10.00N 155:31:50.00E

41:48:17.00N 150:01:05.00E

40:08:11.00N 145:49:00.00E

35:58:42.00N 144:14:18.00E

35:12:00.00N 143:41:37.00E

33:50:10.00N 142:34:54.00E

32:34:59.00N 141:23:40.00E

31:33:54.00N 140:05:40.00E

31:14:18.00N 137:11:36.00E

UNCLASSIFIED

C1/1 UNCLASSIFIED DND SBS DRD - V1.0 - 28 February 2017

Annex C: Classified Space-Based Surveillance Requirements OPI for Classified Space-Based Surveillance Requirements is Comd CFINTCOM.


LAST PAGE OF DOCUMENT

DOCUMENT CONTROL DATA

*Security markings for the title, authors, abstract and keywords must be entered when the document is sensitive

1. ORIGINATOR (Name and address of the organization preparing the document. A DRDC Centre sponsoring a contractor's report, or tasking agency, is entered in Section 8.)

C-CORE Capt Robert A. Bartlett Building 1 Morrissey Rd St. John’s, Newfoundland-and-Labrador Canada, A1B 3X5

2a. SECURITY MARKING (Overall security marking of the document including special supplemental markings if applicable.)

CAN UNCLASSIFIED

2b. CONTROLLED GOODS

NON-CONTROLLED GOODS DMC A

3. TITLE (The document title and sub-title as indicated on the title page.)

Multi-Satellite Data Integration for Operational Ship Detection, Identification and Tracking (DIOS): Progress Report 2

4. AUTHORS (Last name, followed by initials – ranks, titles, etc., not to be used)

Warren, S.; Power, D.; Puestow, T.; Zakharov, I.; Kapfer, M.; Howell, M.; Lynch, M.; Burke, P.; Hewitt, R.

5. DATE OF PUBLICATION (Month and year of publication of document.)

May 2019

6a. NO. OF PAGES

(Total pages, including Annexes, excluding DCD, covering and verso pages.)

127

6b. NO. OF REFS

(Total references cited.)

20

7. DOCUMENT CATEGORY (e.g., Scientific Report, Contract Report, Scientific Letter.)

Contract Report

8. SPONSORING CENTRE (The name and address of the department project office or laboratory sponsoring the research and development.)

DRDC – Valcartier Research Centre Defence Research and Development Canada 2459 route de la Bravoure Québec (Québec) G3J 1X5 Canada

9a. PROJECT OR GRANT NO. (If appropriate, the applicable research and development project or grant number under which the document was written. Please specify whether project or grant.)

05ec - Space-based EO/IR Exploitation (SBEOIRE)

9b. CONTRACT NO. (If appropriate, the applicable number under which the document was written.)

W7714-186608/001/sv

10a. DRDC PUBLICATION NUMBER (The official document number by which the document is identified by the originating activity. This number must be unique to this document.)

DRDC-RDDC-2019-C210

10b. OTHER DOCUMENT NO(s). (Any other numbers which may be assigned this document either by the originator or by the sponsor.)

R-19-011-1443

11a. FUTURE DISTRIBUTION WITHIN CANADA (Approval for further dissemination of the document. Security classification must also be considered.)

Public release

11b. FUTURE DISTRIBUTION OUTSIDE CANADA (Approval for further dissemination of the document. Security classification must also be considered.)

12. KEYWORDS, DESCRIPTORS or IDENTIFIERS (Use semi-colon as a delimiter.)

Maritime Domain Awareness; Arctic; Detection and Target Tracking; Target Detection, Tracking and Multi-Sensor Fusion; Ship and Ice Discrimination

13. ABSTRACT/RÉSUMÉ (When available in the document, the French version of the abstract must be included here.)

This document comprises the Progress Review Report 2 as a deliverable under the Defence Industrial Research Program (DIRP) Contract No. W7714-186608/001/sv. It provides an update on the technical and management process for Multi-Satellite Data Inegration for Operational Ship detection, identification and tracking (DIOS) since the publication of the Progress Review Report 1 delivered on April 2019.

DIOS addresses the Maritime Surveillance Strategic Objective 5 (SO5) by using electro-optical and infrared (EO/IR) sensors to compliment the RADARSAT Constellation Mission (RCM) for ship detection, identification, classification, and tracking. The project investigates new ways of monitoring shipping traffic and icebergs using multiple spaceborne sensors, including RADARSAT-2 (RS-2) and TerraSAR-X (TSX). Progress Review Report 2 describes the methodology used to develop the algorithms required for ship classification using spaceborne radar and electro-optical imagery, and the data collection trials that are planned to be conducted at the Halifax and St. Johns’ harbors in June and July 2019.

Ce document est le Rapport de Progrès 2 en tant que livrable sous le Programme de recherche industrielle pour la défense (PRID) Contrat No. W7714-186608/001/sv. Il présente une mise à jour des progrès techniques pour le project Multi-Satellite Data Integration for Operational Ship detection, identification and tracking (DIOS) depuis la publication du Rapport de Rapport de Progrès 1 livré en avril 2019.

Le projet DIOS adresse l’Objectif Stratégique de surveillance maritime 5 (OS5) en utilisant des capteurs électro-optique et infrarouge (EO/IR) comme complément à la mission de la Constellation RADARSAT pour la détection, l’identification, la classification et le suivi de navires. Le projet investigue de nouvelles façons d’effectuer le suivi de navires et d’icebergs en utilisant des capteurs satellitaires multiples incluant RADARSAT-2 (RS-2) et TerraSAR-X (TSX). Le Rapport de Progrès 2 décrit la méthodologie utilisée pour le développement d’algorithmes de classification de navires à l’aide de capteurs d’imagerie radar et électro-optique, ainsi que la description d’expérimentations qui sont planifiées dans les ports d’Halifax et de Saint-Jean de Terre-Neuve en juin et juillet 2019.

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