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Overview of PCI's software capability for working with SAR imagery
Citation preview
Synthetic Aperture Radar (SAR)PCI expertise and capabilities
January 2013
70 + Employees
> 25,000licenses installed worldwide
HQ: TorontoOffices in:Gatineau, USA, China
60 ResellersWorldwide
Awards & AccoladesInnovation Awards
for
GXLGeomatica
WHERE DOES PCI GEOMATICS
FIT ?
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)
Page 4
WHAT MAKES PCI GEOMATICS
DIFFERENT?
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
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
Page 7
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
Page 8
SAR Sensor Support RADARSAT 1 & 2 TerraSAR-X Cosmo-SkyMed, UAVSAR PALSAR ASAR ERS 1 & 2
Page 9
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
Page 10
Generic SAR Capabilities Supported Calibration Types
• Sigma, • Beta, • Gamma, • None
Multi-Channel Representations• Scattering• Covariance• Coherence• Kennaugh
Page 11
Advantages for applications
Page 12
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
Application examples
Change
Detection
Page 13
Change Detection Methods
1. Amplitude Change Detection
2. Coherence Change Detection
3. Polarimetric Analysis and Change Detection
Page 14
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
Page 15
Change Detection Results
Phoenix AirportSunday May 4, 2008
Change Detection Results
Phoenix AirportWeds. May 28, 2008
Detected Changes
Phoenix AirportChange MapMay 4 , 2008May 28, 2008
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
Page 19
Change in Coherence (phase)
Image 1
Coherent Change Detection
Page 20
Change in Coherence (phase)
Image 2Acquired 11 min. later
Coherent Change Detection
Page 21
Loss of Coherence is indicated by
Dark Colour
Note: Loss of Coherence for Trees
Coherent Change Detection
Page 22
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
Page 23
Flevoland, May 07/2010
RADARSAT-2 Total Power Page 24
Flevoland, May 07/2010
RADARSAT-2 Total Power Page 25
Cross Sensor Change Detection
Optical (Google Map™)
(May 04 - May 07, 2010)
TSX-1/RSAT-2 Change Map
Page 26
Cross Sensor Change Detection
Target May 04 TSX-1/RSAT-2 Change Map
(May 04 - May 07, 2010)
Page 27
Cross Sensor Change Detection
(May 04 - May 07, 2010)
TSX-1/RSAT-2 Change Map No Target May 07
Page 28
Cross Sensor Change Detection
No Target May 04 TSX-1/RSAT-2 Change Map
(May 04 - May 07, 2010)
Page 29
Cross Sensor Change Detection
Optical (Google Map™)
TSX-1/RSAT-2 Change Map
(May 04 - May 07, 2010)
Page 30
Cross Sensor Change Detection
(May 04 - May 07, 2010)
Optical (Google Map™) TSX-1/RSAT-2 Change Map
Page 31
Application examples
Ship detection
(polarimetry)
Page 32
3. Polarimetric Analysis and Change Detection
Basics of Polarimetry Polarimetric information for ship dectection
Page 33
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
Page 34
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
Page 35
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
Page 36
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
Page 37
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
Page 38
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
Page 39
Example Polarimetric Ship Analysis
Page 40
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
Page 41
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
Page 42
Power Distribution
By Polarization HH HV VV
By Type Double Diffuse SurfaceBy Scatterer Primary Secondary Tertiary
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
Page 44
van Zyl Decomposition
Flat Surface
Superstructure
Complex / Random
Physical MeaningRadar Measurement
Odd Number Bounce
Even Number Bounce
Diffuse Scattering
Page 45
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)
Page 46
Classification based upon Polarimetric Signatures ?
1 - 5
6 - 10
11 - 15
16 - 20
1 - 5
6 - 10
11 - 15
16 - 20
Classification based upon Polarimetric Signatures ?
Polarimetric Power Distribution Comparison
Polarization
Type
Scatterer
Page 49
Application examples
Digital Elevation Extraction
Page 50
Multi-Channel Input
HH
HV
VH
VV
Span
Stereo DEMs
R1
R2
Find highest correlation within search window
Compute Stereo Intersection Generate DEM
Geometric Problem
What the Radar Sees
Intermediate Angle
Geometric Problem
Shallow Angle
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 “ *
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
Application examples
Flood Monitoring
Page 57
SAR derived real time flooding information – Manitoba, Canada
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.
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.
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.
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.
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.
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
Assiniboine River April 20, 2011 at PTH 21near previous Radarsat image
Application examples
Ocean Features
Page 66
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.
Page 67
RADARSAT HV in dB
Page 68
Derived Windspeed
Page 69
Page 70
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
Page 71
Contact PCI Geomatics
Page 72
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
@pcigeomatics
www.pcigeomatics.tv
www.facebook.com/pcigeomatics
www.linkedin.com/company/pci-geomatics
www.flickr.com/pcigeomatics