PCI Geomatics Synthetic Aperture Radar Processing Capabilities

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Overview of PCI's software capability for working with SAR imagery

Transcript of PCI Geomatics Synthetic Aperture Radar Processing Capabilities

  • 1. Synthetic Aperture Radar (SAR) PCI expertise and capabilities January 2013

2. 70 + Employees > 25,000 licensesinstalledworldwide HQ: Toronto Awards & AccoladesOffices in: Innovation AwardsGatineau, for60 Resellers USA, ChinaWorldwideGXLGeomatica 3. WHERE DOESPCI GEOMATICSFIT 4. GEOSPATIAL VALUE CHAINImageImage Pre- ImageTools &Value-AddedCollection Processing Processing Work FlowContent Selected CapabilitiesSatellite Orthorectification Image Extraction Display , Storage andGoogle Maps/EarthDisseminationSAR (Radar) AtmosphericSpatial AnalysisMicrosoft Bing MapscorrectionIngestion ToolsLIDARImage ClassificationLocation Based ServicesImage Mosaiking Enterprise IntegrationAirborne CameraCustomizedVertical Applications Pan Sharpening Algorithms Open Sourcenatural resource,Other Image SensingDevelopmentweather, land planning, etc. Selected Competitors/PartnersDigital Globe PCI GeomaticsPCI Geomatics PCI Geomatics Google / Yahoo / MicrosoftVexcel / MicrosoftERDAS (Leica)Definiens AGESRI / Intergraph ESRI / IntergraphGeoEyeENVI (ITT) ERDAS (Leica) Pixel Factory Vertical Applications (e.g., (InfoTerra) RapidEye for Agriculture and ENVI (ITT) Iunctus for Oil and Gas) Page 4 5. WHAT MAKESPCI GEOMATICSDIFFERENT 6. We providePowerful and scalable image processing solutions that let you quickly and efficientlyproduce informationproducts from any typeof imagery SCALABLE TOWE ARE UNMATCHED ANY SIZE SENSOR AUTOMATEDPROJECTAGNOSTICWORKFLOWSBUILDING HIGH SPEEDADVANCED SOLUTIONSMULTI FORRADARCPU / GPUCAPABILITY 30 YEARS 7. WHICH SOLUTION IS RIGHT FOR YOU?$1M $500Price($000s) $200$10 10 GB 100 GB 500 GB 1 - 5TB 5 - 10TBPage 7 8. PCI SAR technology development Canada has been an innovator in SAR dataacquisition and processing since the early1980s PCI has been involved since thebeginning PCI Geomatics participated in GlobeSARprogram, delivered training and software PCI Geomatics developed technologythrough Canadian Government (SARPolarimetry Workstation) PCI Geomatics works with multi-sensor SARimagery Page 8 9. SAR Sensor Support RADARSAT 1 & 2 TerraSAR-X Cosmo-SkyMed, UAVSAR PALSAR ASAR ERS 1 & 2 Page 9 10. Generic SAR Capabilities Support for Single, Dual, Quad, Data Automatic Calibration* Automatic Geocoding* Speckle Filtering (many) Statistical & Analysis Capabilities Ortho-rectification, Integration andVisualization with Optical Data* If available Page 10 11. Generic SAR Capabilities Supported Calibration Types Sigma, Beta, Gamma, None Multi-Channel Representations Scattering Covariance Coherence KennaughPage 11 12. Advantages for applications 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 GenerationPage 12 13. Application examples ChangeDetection Page 13 14. Change Detection Methods1. Amplitude Change Detection2. Coherence Change Detection3. Polarimetric Analysis and Change DetectionPage 14 15. 1. Amplitude Change Detection Different sensors / beam modes /resolutions can be used in combination Revisit is more important in this case thanmatching geometry Presence / absence of features readilyobservedPage 15 16. Change Detection ResultsPhoenix AirportSunday May 4, 2008 17. Change Detection Results Phoenix AirportWeds. May 28, 2008 18. Detected ChangesPhoenix Airport Change Map May 4 , 2008 May 28, 2008 19. 2. Coherent Change Detection Measures phase differences in SAR signal Geometry must be matching (repeat pass) Multiple collections over same area fromdifferent sensors/orbits can be combined Page 19 20. Coherent Change DetectionChange inCoherence (phase)Image 1 Page 20 21. Coherent Change DetectionChange inCoherence (phase)Image 2Acquired 11 min. later Page 21 22. Coherent Change Detection Loss of Coherence is indicated by Dark ColourNote:Loss of Coherence for Trees Page 22 23. Cross Sensor Change Detection Sample CCD over Flevoland TerraSAR-X and RADARSAT-2acquisitions Two sets of repeat pass collections PCI Technology used to achieve highcross-sensor image registrationPage 23 24. Flevoland, May 07/2010 RADARSAT-2 Total Power Page 24 25. Flevoland, May 07/2010 RADARSAT-2 Total Power Page 25 26. Cross Sensor Change Detection (May 04 - May 07, 2010)Optical (Google Map)TSX-1/RSAT-2 Change Map Page 26 27. Cross Sensor Change Detection(May 04 - May 07, 2010)Target May 04 TSX-1/RSAT-2 Change MapPage 27 28. Cross Sensor Change Detection (May 04 - May 07, 2010)No Target May 07TSX-1/RSAT-2 Change MapPage 28 29. Cross Sensor Change Detection (May 04 - May 07, 2010)No Target May 04 TSX-1/RSAT-2 Change Map Page 29 30. Cross Sensor Change Detection (May 04 - May 07, 2010) TSX-1/RSAT-2 Change MapOptical (Google Map) Page 30 31. Cross Sensor Change Detection(May 04 - May 07, 2010)Optical (Google Map) TSX-1/RSAT-2 Change MapPage 31 32. Application examples Ship detection(polarimetry) Page 32 33. 3. Polarimetric Analysis and Change Detection Basics of Polarimetry Polarimetric information for ship dectection Page 33 34. Some Polarimetric BasicsVFor a singlepolarization, thereturn isproportional to thetarget crosssection. HFor HH we wouldget a returnindicated by red.For VV it would beblue.So the amount of return we get depends ontarget orientation and polarizationPage 34 35. Some Polarimetric Basics VFor a singlepolarization, thereturn isproportional to thetarget crosssection.For HH we would Hget a returnindicated by red.For VV it would beblue.So the amount of return we get depends ontarget orientation and polarizationPage 35 36. Some Polarimetric Basics X Polarimetric radar data provides full scattering information in the direction of the line of sight YYX We want to compare these targets. Page 36 37. Some Polarimetric BasicsPolarimetric radardata provides fullscattering informationin the direction of theY line of sight YHX YXH 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.Page 37 38. Non-polarimetric Parameters Time2001-02-30 12:34:56 GMT Position: 12:01:21.58 N 34:14:43.37 W Incidence Angle:27.15 Estimated Length: 226 m Estimated Heading:260 Estimated Velocity: 9.70 m/s Page 38 39. Polarimetric Processing Steps Ingest Full Polarimetric Data (Optionally) calibrate to 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 SPTAfrom remaining (non-masked) pixelsPage 39 40. Example Polarimetric Ship Analysis Page 40 41. Polarimetric InformationMaximum of the degree of polarization: 0.7916655Minimum of the degree of polarization: 0.09595539Maximum of the completely polarized component:2.520944Minimum of the completely polarized component:0.2940039Orientation of Maximum Polarisation 70Ellipticity of Maximum Polarisation -5Maximum of the completely unpolarized component: 2.769960Minimum of the completely unpolarized component: 0.6619406Maximum of the scattered intensity: 3.210612 LLMinimum of the scattered intensity: 2.850842Coefficient of Variation: 0.1160221Fractional Power: 0.7920792HH VVPedestal Height1.318336HH / HV Ratio4.014223HH / HV Correlation 0.2035844RRHH / VV Ratio0.9518262Page 41HH / VV Correlation 0.3857002 42. Polarimetric Signature InformationV LL 5 Ellipticity70 Orientation H VVMaximum ReturnV LL RR SecondaryHHReturnMax Return H- 20 OrientationStrong Secondary ReturnPage 42RR 43. Power DistributionBy PolarizationHH HVVVBy TypeDoubleDiffuseSurfaceBy Scatterer Primary SecondaryTertiary 44. Polarimetric Decompositions Cloude-PottierTarget Average % High % Medium% LowEntropy 0.8480822 2.25330276.30148 21.44522Anisotropy0.5064220 55.63326 44.36674Alpha Angle 43.200062 27.50583 30.5361341.95804Touzi (ICTD)Target Tilt Dominant Eigen SymmetricSymmetricHelicityAngle ValueScattering TypeScattering Type(Symmetry)(deg)Magnitude Phase(deg)-27.4323730.560099210.467688-50.4832465.841676 van Zyl % Unclassified% Odd% Even % Diffuse 1.89274448.264984 23.34384926.498423Page 44 45. van Zyl DecompositionRadar Measurement Physical Meaning Odd Number BounceFlat Surface Even Number Bounce Superstructure Diffuse Scattering Complex / Random Page 45 46. 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 47. Classification based upon PolarimetricSignatures ?1-56 - 1011 - 1516 - 20 48. Classification based upon PolarimetricSignatures ?1-56 - 1011 - 1516 - 20 49. Polarimetric Power Distribution ComparisonPolarization TypeScatterer Page 49 50. Application examplesDigital Elevation Extraction Page 50 51. Multi-Channel InputHHHVSpanVHVV 52. Stereo DEMsFind highest correlation within search windowR1R2Compute Stereo Intersection Generate DEM 53. Geometric ProblemIntermediate Angle What the Radar Sees 54. Geometric ProblemShallowAngle 55. Stereo DEMs All orMaximum Overlap Next Pair Image match based upon Power Linear or DecibelsImage A Image BNoOverlap, Look DirectionAngular Difference Suitable Pair ? Downsample Image ADownsample Image Bto User Specs to Epipolar Image ASpacing Affects DEMDetail Level Extract Window AreaExtract Search AreaIgnore BackgroundFind Common