Download - PCI Geomatics Synthetic Aperture Radar Processing Capabilities

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Page 1: PCI Geomatics Synthetic Aperture Radar Processing Capabilities

Synthetic Aperture Radar (SAR)PCI expertise and capabilities

January 2013

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70 + Employees

> 25,000licenses installed worldwide

HQ: TorontoOffices in:Gatineau, USA, China

60 ResellersWorldwide

Awards & AccoladesInnovation Awards

for

GXLGeomatica

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WHERE DOES PCI GEOMATICS

FIT ?

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GEOSPATIAL VALUE CHAIN

Image Collection

Image Pre-Processing

Selected Competitors/Partners

Digital Globe

Vexcel / Microsoft

GeoEye

PCI Geomatics

ERDAS (Leica)

ENVI (ITT)

PCI Geomatics

ESRI / Intergraph

Pixel Factory (InfoTerra)

Selected Capabilities

Google / Yahoo / Microsoft

ESRI / Intergraph

Vertical Applications (e.g., RapidEye for Agriculture and Iunctus for Oil and Gas)

Image Processing

Tools & Work Flow

Value-Added Content

Satellite

SAR (Radar)

LIDAR

Airborne Camera

Other Image Sensing

Orthorectification

Atmospheric correction

Image Mosaiking

Pan Sharpening

Display , Storage and Dissemination

Ingestion Tools

Enterprise Integration

Open Source Development

Google Maps/Earth

Microsoft Bing Maps

Location Based Services

Vertical Applications – natural resource, weather, land planning, etc.

Image Extraction

Spatial Analysis

Image Classification

Customized Algorithms

PCI Geomatics

Definiens AG

ERDAS (Leica)

ENVI (ITT)

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WHAT MAKES PCI GEOMATICS

DIFFERENT?

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We provide…Powerful and scalable

image processing solutions that let you quickly and efficiently produce information

products from any type of imagery

UNMATCHEDAUTOMATEDWORKFLOWS

WE ARESENSOR

AGNOSTIC

ADVANCEDRADAR

CAPABILITY

HIGH SPEEDMULTI

CPU / GPU

BUILDINGSOLUTIONS

FOR

30 YEARS

SCALABLE TO

ANY SIZEPROJECT

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WHICH SOLUTION IS RIGHT FOR YOU?

10 GB 1 - 5TB 5 - 10TB

$10

$200

$500

Price ($000’s)

100 GB

$1M

500 GB

Lower volume

H

igher Volume

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PCI – SAR technology development

Canada has been an innovator in SAR data acquisition and processing since the early 1980s – PCI has been involved since the beginning

PCI Geomatics participated in GlobeSAR program, delivered training and software

PCI Geomatics developed technology through Canadian Government (SAR Polarimetry Workstation)

PCI Geomatics works with multi-sensor SAR imagery

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SAR Sensor Support RADARSAT 1 & 2 TerraSAR-X Cosmo-SkyMed, UAVSAR PALSAR ASAR ERS 1 & 2

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Generic SAR Capabilities Support for Single, Dual, Quad, Data Automatic Calibration* Automatic Geocoding* Speckle Filtering (many) Statistical & Analysis Capabilities Ortho-rectification, Integration and

Visualization with Optical Data * If available

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Generic SAR Capabilities Supported Calibration Types

• Sigma, • Beta, • Gamma, • None

Multi-Channel Representations• Scattering• Covariance• Coherence• Kennaugh

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Advantages for applications

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Key Advantages of Commercial Radar Imagery– Data collections are independent of lighting and cloud conditions– Frequent imaging supports routine change detection– Provides effective wide area (100 –500+ km swath) coverage – A variety of information is contained in the return signal that can be

extracted

Key Maritime Missions:– Large Area Maritime Domain Awareness– Efficient Tasking of Patrol Assets– Monitoring Port Activity

Key Terrestrial Missions:– Change Detection – Disaster Response – DEM Generation

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Application examples

Change

Detection

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Change Detection Methods

1. Amplitude Change Detection

2. Coherence Change Detection

3. Polarimetric Analysis and Change Detection

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1. Amplitude Change Detection

Different sensors / beam modes / resolutions can be used in combination

Revisit is more important in this case than matching geometry

Presence / absence of features readily observed

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Change Detection Results

Phoenix AirportSunday May 4, 2008

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Change Detection Results

Phoenix AirportWeds. May 28, 2008

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Detected Changes

Phoenix AirportChange MapMay 4 , 2008May 28, 2008

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2. Coherent Change Detection

Measures phase differences in SAR signal Geometry must be matching (repeat pass) Multiple collections over same area from

different sensors/orbits can be combined

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Change in Coherence (phase)

Image 1

Coherent Change Detection

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Change in Coherence (phase)

Image 2Acquired 11 min. later

Coherent Change Detection

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Loss of Coherence is indicated by

Dark Colour

Note: Loss of Coherence for Trees

Coherent Change Detection

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Cross Sensor Change Detection

Sample CCD over Flevoland TerraSAR-X and RADARSAT-2

acquisitions Two sets of repeat pass collections PCI Technology used to achieve high

cross-sensor image registration

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Flevoland, May 07/2010

RADARSAT-2 Total Power Page 24

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Flevoland, May 07/2010

RADARSAT-2 Total Power Page 25

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Cross Sensor Change Detection

Optical (Google Map™)

(May 04 - May 07, 2010)

TSX-1/RSAT-2 Change Map

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Cross Sensor Change Detection

Target May 04 TSX-1/RSAT-2 Change Map

(May 04 - May 07, 2010)

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Cross Sensor Change Detection

(May 04 - May 07, 2010)

TSX-1/RSAT-2 Change Map No Target May 07

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Cross Sensor Change Detection

No Target May 04 TSX-1/RSAT-2 Change Map

(May 04 - May 07, 2010)

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Cross Sensor Change Detection

Optical (Google Map™)

TSX-1/RSAT-2 Change Map

(May 04 - May 07, 2010)

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Cross Sensor Change Detection

(May 04 - May 07, 2010)

Optical (Google Map™) TSX-1/RSAT-2 Change Map

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Application examples

Ship detection

(polarimetry)

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3. Polarimetric Analysis and Change Detection

Basics of Polarimetry Polarimetric information for ship dectection

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For a single polarization, the return is proportional to the target cross section.

For HH we would get a return indicated by red.

For VV it would be blue.

V

H

So the amount of return we get depends on target orientation and polarization

Some Polarimetric Basics

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H

V

So the amount of return we get depends on target orientation and polarization

For a single polarization, the return is proportional to the target cross section.

For HH we would get a return indicated by red.

For VV it would be blue.

Some Polarimetric Basics

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We want to compare these targets.

X

Y

XY

Polarimetric radar data provides full scattering information in the direction of the line of sight

Some Polarimetric Basics

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H

HY

Y

X

X

Y

We can do some fancy arithmetic and rotate the scattering matrix until we get a maximum X and a minimum Y.

Then we can compare their properties.

Polarimetric radar data provides full scattering information in the direction of the line of sight

Some Polarimetric Basics

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Non-polarimetric Parameters

Time 2001-02-30 12:34:56 GMTPosition: 12:01:21.58 N 34:14:43.37 WIncidence Angle: 27.15°Estimated Length: 226 mEstimated Heading: 260°Estimated Velocity: 9.70 m/s

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Polarimetric Processing Steps

Ingest Full Polarimetric Data (Optionally) calibrate to σ° Apply multi-channel speckle filter Decompose (Cloude-Pottier) image into (16) polarimetric classes Iterate (3-5 times) to enhance classification and remove outliers Exclude pixels from the largest class (which will be water) Generate land mask * Generate polarimetric parameters using FOCUS, SPW and SPTA

from remaining (non-masked) pixels

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Example Polarimetric Ship Analysis

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Polarimetric InformationMaximum of the degree of polarization: 0.7916655Minimum of the degree of polarization: 0.09595539

Maximum of the completely polarized component: 2.520944Minimum of the completely polarized component: 0.2940039

Orientation of Maximum Polarisation 70

Ellipticity of Maximum Polarisation -5

Maximum of the completely unpolarized component: 2.769960Minimum of the completely unpolarized component: 0.6619406

Maximum of the scattered intensity: 3.210612

Minimum of the scattered intensity: 2.850842

Coefficient of Variation: 0.1160221

Fractional Power: 0.7920792

Pedestal Height 1.318336

HH / HV Ratio 4.014223HH / HV Correlation 0.2035844

HH / VV Ratio 0.9518262HH / VV Correlation 0.3857002

RR

LL

VVHH

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Polarimetric Signature Information

HH

RR

LL

VV

5° Ellipticity

H

V

70° Orientation

Maximum Return

H

V

- 20° Orientation

Strong Secondary Return

RR

LL

Secondary Return Max

Return

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Power Distribution

By Polarization HH HV VV

By Type Double Diffuse SurfaceBy Scatterer Primary Secondary Tertiary

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Polarimetric DecompositionsCloude-Pottier

  Target Average % High % Medium % LowEntropy 0.8480822 2.253302 76.30148 21.44522

Anisotropy 0.5064220 55.63326   44.36674Alpha Angle 43.200062 27.50583 30.53613 41.95804

Touzi  (ICTD)Target Tilt Angle(deg)

Dominant Eigen Value

Symmetric Scattering Type Magnitude

Symmetric Scattering Type 

Phase

Helicity (Symmetry)

(deg)

-27.432373 0.5600992 10.467688 -50.483246 5.841676

van Zyl% Unclassified  % Odd  % Even % Diffuse

1.892744 48.264984 23.343849 26.498423

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van Zyl Decomposition

Flat Surface

Superstructure

Complex / Random

Physical MeaningRadar Measurement

Odd Number Bounce

Even Number Bounce

Diffuse Scattering

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Symmetric Scattering Decomposition

Trihedral (odd number of bounces)

Cylinder (weak return in one direction)

Dipole (no return in one direction)

Quarter Wave (delay in second direction)

Dihedral (even number of bounces)

Narrow Dihedral (with one direction attenuated)

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Classification based upon Polarimetric Signatures ?

1 - 5

6 - 10

11 - 15

16 - 20

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1 - 5

6 - 10

11 - 15

16 - 20

Classification based upon Polarimetric Signatures ?

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Polarimetric Power Distribution Comparison

Polarization

Type

Scatterer

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Application examples

Digital Elevation Extraction

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Multi-Channel Input

HH

HV

VH

VV

Span

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Stereo DEMs

R1

R2

Find highest correlation within search window

Compute Stereo Intersection Generate DEM

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Geometric Problem

What the Radar Sees

Intermediate Angle

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Geometric Problem

Shallow Angle

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Stereo DEMs

Image A Image B

Suitable Pair ?

Downsample Image A to User Specs

Downsample Image B to Epipolar Image A

Find Common Points

Extract Window Area Extract Search Area

Stereo Intersection

Next Pair

Last Pair ?

Store Elevation

Arbitrate Values

Fill Gaps/Holes

No

No

Overlap, Look DirectionAngular Difference

Blend Overlap AreasLast, Average, High Score

Image match based upon Power Linear or Decibels

Write Final DEM

Spacing Affects DEM Detail Level

All or Maximum Overlap

Write Failed Value where “gaps” remain

Ignore Background (No Data) Pixels

Remove “buildings “ *

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Suggestions for Selection of Stereo Pairs

Selection of Stereo Image Pairs

Candidate pair should have more than 50 % overlap Candidate pair should have nominally the same resolution Best results obtained from same-side (i.e., descending/descending or

ascending/ascending) image pairs Candidate pair should have matching polarizations Large incident angle (i.e., S7 ) are preferable (to minimize terrain

displacement effects) The larger the difference between incident angles, the greater the

parallax in the stereo pair (recommend 5°- 25° angular difference) Opposite-side (i.e., ascending/descending) image pairs only

recommended for very low relief areas; with similar tonal characteristics

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Application examples

Flood Monitoring

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SAR derived real time flooding information – Manitoba, Canada

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RADARSAT-2 acquisition April 15, 2011 - 00:11 UTC

Red River

© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

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RADARSAT-2 acquisition April 18, 2011 - 12:32 UTC

Red River

© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

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RADARSAT-1 acquisitionApril 20, 2011 - 00:15 UTC

Red River

© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

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RADARSAT-2 acquisition April 22, 2011 - 00:07 UTC

Red River

© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

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RADARSAT-2 acquisition April 25, 2011 - 12:27 UTC

Red River

© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

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RADARSAT-2 acquisitionApril 21, 2011 - 00:36 UTC

Assiniboine River

© Her Majesty the Queen in Right of Canada, Department of Natural Resources. All rights reserved.

Approximate location of air photo

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Assiniboine River April 20, 2011 at PTH 21near previous Radarsat image

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Application examples

Ocean Features

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Wind Speed Analysis

Steps: #1: Convert to calibrated data (SARINGEST) #2: Boxcar Filter (19 x 19) #3: Convert filtered HV data to decibel #4: If HV data ( < -21 dB) apply Paris Vachon

algorithm to generate Windspeed in m/s.

Purple = 10 m/s to red = 16 m/s.

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RADARSAT HV in dB

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Derived Windspeed

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Summary of PCI Capabilities

Software / scalable Geomatica Radar Suite

www.pcigeomatics.com/sar Includes SPW and Target

Analysis Ingest, correct Multi-sensor

SAR data

SAR for GXL Custom implementation of

SAR analysis for large volume processing

Experience/ know-how Dedicated development

team Senior SAR scientist on

staff 30 years of experience SAR training available

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Contact PCI Geomatics

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TORONTO50 West Wilmot

Richmond Hill, ON

Canada, M4B 1M5

Phone: (905) 764-0614

Fax: (905) 764-9064

GATINEAU490 St-Joseph Boulevard

Gatineau, QC

Canada, J8Y 3Y6

Phone: (819) 770-0022

Fax: (905) 770-0098

www.pcigeomatics.com

[email protected]

@pcigeomatics

www.pcigeomatics.tv

www.facebook.com/pcigeomatics

www.linkedin.com/company/pci-geomatics

www.flickr.com/pcigeomatics