Definiens Enterprise Image Intelligence TechnologyDefiniens Enterprise Image Intelligence Technology...

Definiens Enterprise Image Intelligence Technology

Dr. Waldemar Krebs – Account Manager Earth Sciences

Prag - Jan. 2008

Remote Sensing Imagery: A Great Source for Geointelligence

• From overview to detail

• Recent information on any areas around the world

• More than eyes can see

Extract Relevant Information in Time: The Great Challenge

• Select within a tsunami of imagerythe right data set and area within the imagery

• Provide objective analysis based on challenging data under huge time pressure

• Automation of the human expertise has proved challenging or impossible before now

Multi SourceDate Provision Geo - Intelligence

Information Extraction: The Bottleneck

Change Detection

Target Detection

Mapping

Target Recognition

GIS

Thermal

RADAR / LIDAR

OpticImagery

overwhelming volume of data

highly experienced image analystrequired to screen huge amounts of data

time consuming & error prone

available systems for automation areproprietary solutions for single applicationswith huge maintenance costs

critical information is detectedtoo late or not at all

expensive quality assurance process

no accurate information extractionwith reasonable automation degree available

Information Extraction

Definiens CognitionNetwork Technology ®

Multi SourceDataProvision

Geo - Intelligence

The Solution: Geo-Intelligence Supply Chain using eCognition

Change Detection

Target Detection

Mapping

Target Recognition

GIS

Thermal

RADAR /LIDAR

OpticImagery

Information Extraction

Implemented in Definiens Enterprise Image Intelligence® Suite

1995: Research ‚Think-Tank‘Created by Physics Nobel Prize Laureate Prof. Dr. Gerd Binnig

1998: Cognition Network Technology (CNT)20+ international patents

2000: CommercializationFunded by TVM and CIPIO Partners

2003: Focus on Image Intelligence

2004: Focus on Life Sciences and Geospatial Intelligence

2007: Staff 80+, 2500+ licenses worldwide,

HQ Munich, US operations

Company Background

About Definiens Enterprise Image Intelligence

� Definiens Enterprise Image Intelligence is the only technology that

understands images similar to the human mind.

� Definiens provides breakthrough Enterprise Image Intelligence to customers

like NGA, Lockheed Martin, DigitalGlobe, European Commission etc.

� Other technologies are still focused on single PC performance and pixel

based image analysis, whereas Definiens uses object and context based

technology.

� Definiens Image Intelligence provides scalable solutions from laptop to

production centre and thus guarantees maximum performance.

Applications Overview

Urban MonitoringUrban Land Cover Mapping

GMES-GUS

Semi-Automatic Land Use Classification

17%

13%

4%

4%

0%

9%2%

16%

6%

6%

13%

1%

9%

1.1.1.1 Residential continuous dense urban fabric

1.1.1.2 Residential continuous medium dense urban fabric

1.1.2.1 Residential discontinuous urban fabric

1.1.2.2 Residential discontinuous sparse urban fabric

1.2 Industrial, commercial and transport units / 1.3 Mine,dump and construction sites1.2.2.1 Road networks and associated land

1.2.2.2 Rail networks and associated land

1.4 Artificial non agricultural vegetated areas

2.1 Arable land

2.3 Pastures

3.1 Forests / 2.2 Permanent crops / 3.2 shrub and/orherbaceous vegetation associations4. Wetlands

5. Water bodies

17%

13%

4%

4%

0%

9%2%

16%

6%

6%

13%

1%

9%





1.2 Industrial, commercial and transport units / 1.3 Mine,dump and construction sites1.2.2.1 Road networks and associated land



2.1 Arable land

2.3 Pastures

3.1 Forests / 2.2 Permanent crops / 3.2 shrub and/orherbaceous vegetation associations4. Wetlands

5. Water bodies

GUS - Consortia - funded by European Space Agency (ESA)

Urban Land Use – Present Land Use

Legend

1.1.1 Continuous urban fabric



1.1.2 Discontinuous urban fabric



1.2 Industrial, commercial and transport units /1.3 Mine, dump and construction sites

1.2.2 Road and rail networks and associated land

1.2.2.1 Road networks and associated land



2.1 Arable land

2.3 Pastures

3.1 Forests / 2.2 Permanent crops /3.2 Shrub and/or herbaceous vegetation associations

4. Wetlands

5. Water bodies

GMES Urban Services

Urban Land Use – Present Land Use

Statistics

8.87 19.84 5. Water bodies

0.86 1.92 4. Wetlands

12.91 28.86 3.1 Forests / 2.2 Permanent crops / 3.2 Shrub and/or herbaceous vegetation associations

6.31 14.11 2.3 Pastures

6.14 13.73 2.1 Arable land

15.62 34.93 1.4 Artificial non agricultural vegetated areas

1.83 4.08 1.2.2.2 Rail networks and associated land

9.45 21.13 1.2.2.1 Road networks and associated land

0.07 0.16 1.2 Industrial, commercial and transport units / 1.3 Mine, dump and construction sites

3.73 8.34 1.1.2.2 Residential discontinuous sparse urban fabric

4.20 9.40 1.1.2.1 Residential discontinuous urban fabric

13.22 29.57 1.1.1.2 Residential continuous medium dense urban fabric

16.75 37.46 1.1.1.1 Residential continuous dense urban fabric

Portion of overall area [%]

Overall area [km²]

17%

13%

4%

4%

0%

9%2%

16%

6%

6%

13%

1%

9%

GMES Urban Services

1. Spot 5

pan: 2,5m resolution

ms: 10m resolution

(nir, red, green, swir)

2. Landsat ETM

pan: 15m resolution

ms: 30m resolution

(b, g, r, nir, swir, mir, thermal)

Input Data - Raster

Munich

Munich

Input Data – DEM

SRTM (SAR Radar Topography Mission)Ground Resolution: 90

referenced in: UTM, WGS84

tiled in: 1201 x 1201 pixel

Covering: world wide

Dresden

Input Data – Vector Data

1. TeleAtlas (used in navigation systems)Format: *.shp file / standard GIS format

Available for Western Europe and United States

Used features: Road network in 8 classes (from motorway to local road)

2. NavTeq (used in navigation systems)Format: *.shp file /standard GIS format

(possible using in future)

3. Digitized Road systemdigitized from Quickbird-Data (Barcelona) / Ikonos-Data aerial photos / using ArcGIS/ArcView

4. Customers delivery

Classification Data - Classes

Residential denseurban fabric

Residential mediumdense urban fabric

Residential sparseurban fabric

Industrial / Commercial / Public

Urban Trees

Classification – Results

Example Munich

Classification – Roads and Houses

Classification – Urban Structures

Classification – Urban Landuse

Classification – Impervious Areas

Useful for:

� Land consumption

� Flood protection

Forestry

eCognition Forester

eCeC--ForesterForester

TreeTree CountingCounting

inYoungForestStandsinYoungForestStandstest site: Scotland

2006 trees/ha

A study for Foresty Commission / Stewart Snape by Definiens AG

eCognition Forester 1.1

A study for Forestry Commission of Great Britain

tree topsHigh resolution ortho-photo (RGB) Exportable classification results:

subset

0 250 500 [m]

2006 trees/ha2006 trees/ha

1844 trees/ha1844 trees/ha

Processing time: 30 sec/ha Inaccuracy: + - 5%

eCognition Forester

Automatic classification of tree tops in forested areas

Automatic export of results to GIS

Visualisation of tree tops with graduated symbols, e.g. based on area of tree tops

Workflow

eCognition Forester

Transfer of rule base to ortho-photos of different areas with appropriate

additional thematic layers.

Transferability

2006 trees/ha

Subsets of ortho-photo Classification result Exported tree position

eCognition Forester

Transfer of rule base to ortho-photos of different areas with appropriate

additional thematic layers.

Transferability

3244 trees/ha

3244 trees/ha

2875 trees/ha


eCognition Forester

Transfer of rule base to ortho-photos of different areas with

appropriate additional thematic layers.

Transferability

2460 trees/ha


eCognition Forester

Have a look at details of a subset

eCognition Forester

Details - Input dataDetails - Classification of single trees

Have a look at further details ��

eCognition Forester

Further details –Classification of single trees

eCognition Forester

Further details –Classification of single trees

Definiens & Silvatech developed a robust strategy for new cutblock identification usingmultitemporal Landsat5 and Landsat7 data.

eCognition technology provides� Robust change detection based on satellite imagery

� Efficient fusion of different satellite imagery and ancillaryinformation like DEM.

� Automated extraction of polygons leads to signficant time savings

� Definiens Professional Services allowed� Silvatech to explore the full potential of eCognition and

achieve impressive results in a short time frame -fully satisfying their customer (MSRM).

10 km

Consultancy project carried out for: 1) MSRM: Ministry of Sustainable Resource Management, British Columbia - Terrestrial Information Branch

DEMLandsat 5 dataset

Input data

Cutblock Water

Landsat 7 dataset

Canada - Cutblock Inventory

Cutblock Inventory in British Columbia for MSRM1 Consultancy project together with SilvatechConsulting Ltd.

Clear cut extraction withcurrent technology

clear cut extraction with eCognition‘s polygonsLandsat data set (5 and 7)

GIS procedures for extracting inventory polygons from satellite remote sensing data


Different input datasets are used to optimize results

Landsat scene Exported results in *.shp format

Development of eCognition rule bases on subsets

Utilization of existing satellite information along with auxiliary data

Automatic generation of geo-information for entire image mosaic

Robust Extraction of Clearcuts from Multi-temporal Landsat Data


Developed Prototype:

• automatically creates polygon shape files representing cutblockpolygons, coastlines and water

• delivers according statistic

• saves developed strategy in protocol file for automatic reuse

Output shp files of initial data set(Rivers Inlet, BC – June 2000)

Clear Cut statistics over whole area

CutblockWater

Class cutblock water_L3 Objects 137 96Sum Area 3.20E+07 1.68E+08Mean Area 233489 1.75E+06StdDev Area 203644 1.07E+07Min Area 10350 11250Max Area 1.03E+06 1.02E+08Sum Length 132280 128810Mean Length 965.548 1341.77

Second Data set (Kamloops BC,- Aug 2001)

Robust technology allows successful strategy transfer


Pipeline Planning

Export Suitability Map

shp. with attributes

Schematic Workflow

data fusion

vectorlayer(GIS)

image layer

Input Processing Final Result

data input result

landcoveranalysisEO data

GIS analysis+ distances

slopecalcualation

EO data

Suitability Map

•suitability classes•GIS attributes•context attributes•experts comments

action

Analysis of Input Data

Intersection and Categorization

Overall Concept

� using cost effective, publicavailable data

� coarse evaluation of suitable/risky areas

� the in Level 1 identified areasare detailied analysed

� additional more detailied GIS and remote sensing data areused

Level 2:Detailed Suitability/Risk Calculation

Level 1:Coarse Suitability/Risk Calculation

Level 1: Coarse Suitability

Coarse Suitability Map

the used data are free of charge and public avaliable

Input 1. Analysis

e.g. streets, existing tracks, soil map,….

slope calculation

up to date land cover

suitability criteria in

eCognition PIPEMON

high suitability

good suitability

limited suitability

reduced suitability

excluded

GIS

Elevation

Landsat

Level 2: Detailed Suitability

land use, risks, legal restrictions, infrastructure…

land cover

information derived in Level 1 analysis

GIS

Level1

Aerial Image

Detailed Suitability MapInput 1. Analysis

Suitability criteria in

eCognition PIPEMON

high suitability

good suitability

limited suitability

reduced suitability

excluded

GUI eCognition PIPEMON – Test site Dorsten

eCognition PIPEMON - Steps to Suitability Map


Conclusion

�Flexible: use any kind of GIS and remotesensing data available

�Scalable:different levels of detail possible

�GIS Data are updated and areas evaluated

�Expert knowledge can easilybe implemented and retracably administrated

�Exported results contain all relevant information used forsuitability/risk assessment

SuitabilityHigh SuitabliltyGood SuitabliltyReduced SuitabliltyLimited SuitabliltyExcluded

010001

11.39909

04_limitedsuitability6

010001

11.39909


000101

10.7790902_goodsuitable4

000101


000101


010001

11.62909


010001

11.62909


exclude

limited

reduced

Good suited

High suited

Slope

srtm

Class_ID

Layer

Handle Classid

Route Planning in Arid Areas

Feasibility Study for EADS - Dornier

Data: Cheap and nearly everywhere available

• Aster Level 1B VNIR• Radiometric resolution: 3 bands• Spatial resolution: 15m

Automatic extraction of rivers and wadis to enable

• manual route planning for different routes on actual data sets

• identify, count and report cross-overs


river bed / Wadis 0 5 10 km

River and wadi extractionin 3.6 s/ km2 with high accuracyon Windows XP; Pentium 4; 1,4 GHz; 2 GB of RAM:

Use extracted featuresin mobile environment

• Export of river bed polygons(ESRI Shape)

• Easy post-processing• Transfer to PDA possible for

mobile use

Next Steps

64Route 3 Crossing River/Wadi




Marine Applications

Off Shore Oil Detection

Prestige Tanker Disaster (Coast of Galicia, Spain)

Data: ASAR Wide Swath

Oil and Coast Line Detection

Security Applications and Projects

Road centerline:

Rapid Mapping – Mobility Database

Basic landcover classification

Successful test at Fort Knox: Fully automated map production using eCognition

automatedroad detection

&centerline

export

Route planning

Analysis for Route Planning

+ Landuse based on IKONOS+ Slope Analysis based on SRTM+ Soil type using USGS tpye+ actual weather using METARS

criteria forGO/No GO

Information layer combininginformation from multiple sources

Rapid Mapping – Mobility Database

Workflow: Fully automated map production using eCognition based using EO imagery

Ship Detection

Norfolk SubsetsAssisted Ship Detection / Recognition

Data courtesy: Digital Globe, work partly funded by RESTEC and NGA

Automated AttributionAssisted Ship Detection / Recognition

Data courtesy: Digital Globe, work partly funded by RESTEC

On screen review

Execution and quality control with eCognition Analyst

Information on processed images

Information on created tiles

Number of detected Ships /Submarines

Used processor

Tiled image

Complete image

Assisted AirplaneDetection / Recognition

Image Resolution: 11.2 cm

Automated Airplane Detection in Airborne Imagery

Data courtesy and work funded by: DRDC - Defense Research and Development Canada Valcartier Optronic Surveillance, Data Exploitation Group

Assisted Airplane Detection / Recognition


Automated Airplane Detection – aerial photography

Automated Airplane Detection in Airborne Imagery


Wizard Guided Target Recognitionand GIS Integration

Evaluation of Wizard Guided Target Recognition in Aerial Imagery


Automated Vehicle Detection

Image Data Results

High Resolution Airborne Off-Nadir 30º Image

Automated Vehicle Detection

nadir 30 degrees

parking lotvehicles

High Resolution Airborne Nadir and Off-Nadir Images

Vehicle Detection with ATRc eCognition Wizard

�eCognition Rulebase forVehicle Detectionusing spectral + shape features

�Classification

�InputHigh Resoluted Airborne Data different Angles

�Export as Statisticsand for GIS


Video

Detection Results on Video Stream of Infrared Imagery

detected vehiclehit missed false alarm

Conclusion

Unique and Proven TechnologyModels Human Cognition, context and understanding

Secure and ReliableObject based reliability, consistent quality, IP protection

Fully ScalableFrom Single Workstation to Enterprise Level

Multi Source IntelligenceMulti data and information fusion

Highly FlexibleModular architecture, easy adaptable

Thanks for your attention !

Dr. Waldemar KrebsAccount Manager Earth SciencesDefiniens AG

Phone: +49(0)89 231180-27Fax: +49(0)89 231180-90E-mail [email protected]

Mission

Global leader in Enterprise Image Intelligence

We Understand ImagesTM

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