Don Atwood 1 and David Small 2 University of Alaska Fairbanks University of Zurich, Switzerland

1. USE OF RADIOMETRIC TERRAIN CORRECTION TO IMPROVE • POLSAR LAND COVER CLASSIFICATION Don Atwood1 and David Small2 University of Alaska Fairbanks University of Zurich, Switzerland

2. Presentation Overview • Introduce Boreal Land Cover Classification project • Focus on species differentiation in boreal environment • Introduce reference data for land cover classification • Introduce method of Radiometric Terrain Correction (RTC) • Terrain-flattened Gamma Naught Backscatter • Perform RTC on polarimetric parameters to address topography • Demonstrate synergy of PolSARpro and MapReady Tools • Compare results for RTC-corrected and non-corrected classification • Characterize optimal classification approach for Interior Alaska

3. Study Region • Boreal environment of Interior Alaska • Characterized by: • rivers • wetlands • herbaceous tundra • black spruce forests (north facing) • birch forests (south facing) • low intensity urban areas

4. Land Cover Reference

5. Study Data • Quad-Pol data selected: • ALOS L-band PALSAR • 21.5 degree look angle • Of April, May, July, and Nov dates, • July 12 2009 selected • Post-thaw • Leaf-on • Coverage includes Fairbanks and regional roads Pauli Image

6. Problem of Topography Span (Trace of T3 Matrix) Wishart Segmentation

7. Aβ & β0 Aγ & γ0 Aσ & σ0 Normalized Radar Cross Sections Sensor Nadir • Let’s compute Normalized Radar Cross Sections • for an Ellipsoidal Earth • for Topography Near Far

8. Backscatter Reference Areas For an Ellipsoidal Earth Relationships between cross sections for ellipsoidal surfaces

9. Terrain-flattening • We need to move beyond the ellipsoidal Earth to the hills and valleys of the Fairbanks region: • Address the layover and foreshortening of geometric distortions • Correct the radiometric variations associated with topography. • To improve our radiometry: • use local area contributing to backscatter at each location in the SAR scene

10. Terrain-flattening

11. Terrain-flattening Solution: Use simulated image to Normalize β0 X Ref.: Small, D., Flattening Gamma: Radiometric Terrain Correction for SAR Imagery, IEEE Transactions on Geoscience and Remote Sensing, 13p (in press).

12. Terrain Correction in Coastal BC Vancouver GTC (Sept 2008) Integrated contributing area ENVISAT ASAR WSM data courtesy ESA(based on SRTM3)

13. Terrain Correction in Coastal BC GTC (Sept 2008) Integrated contributing area ENVISAT ASAR WSM data courtesy ESA(based on SRTM3)

14. Coastal BC: GTC ASAR WSM GTC

15. Coastal BC: RTC ASAR WSM RTC

16. Coastal BC: NORLIM ASAR WSM NORLIM

17. Coherency Matrix Scattering Matrix : “Double Bounce” : “Single Bounce” : “Volume Scattering”

18. Coherency Matrix terrain corrected Coherency Matrix Area Normalization Radiometric Terrain Correctionof Coherency Matrix • Radiometric Terrain Correction: • Scale all matrix elements by Area Normalization

19. But Wait….. For a given class, the ratio of Surface, Double Bounce, and Volume scattering components depend on incidence angle POLARIMETRIC IMPLICATIONS OF INCIDENCE ANGLE VARIABILITY FOR UAVSAR Guritz, Atwood, Chapman, and Hensley

20. Radiometric Terrain Correctionof Coherency Matrix Span: No Normalization Span: Terrain-model Normalization

21. Radiometric Terrain Correctionof Coherency Matrix Pauli: No Normalization Pauli: Terrain-model Normalization

22. Integration of PolSARpro and MapReady Ingest PALSAR data Terrain-correct Perform WishartExport to GIS Generate T3 with MapReadydecomposition Cluster-busting Radiometric correction using area Lee Sigma Speckle Filter POA compensation

23. Radiometric Terrain Correctionof Coherency Matrix Wishart - No Normalization Wishart - Radiometric Correction

24. Radiometric Terrain Correctionof Coherency Matrix USGS Reference Wishart– Radiometric Correction

25. Classification Results No Normalization USGS Reference RTC

26. Classification Results Urban areas missed / Identified as Open Water

27. Classification Results Inability to distinguish Mixed Forests and Shrub / Scrub

28. Accuracy Assessment No Normalization

29. Accuracy Assessment With RTC

30. Accuracy Assessment Comparison • RTC yields improved accuracy (particularly for Deciduous Forest)

31. Impact of RTC on forest classification No Normalization USGS Reference RTC

32. Conclusions • In general, PolSAR classification is difficult! • Data fusion provides greatest hope for accurate classification results • Radiometric variability caused by topography dominates PolSAR classification • Area-based RTC offers effective way to “flatten” SAR radiometry • RTC of Coherency Matrix shown to improve classification accuracy: • Impact most pronounced for Deciduous Forests • Although not complete, RTC approach is simple and effective • Different scattering mechanisms (SB, DB, Volume) have different sensitivities to topography. RTC does not address this • However, RTC is very effective first order correction for segmenting polarimetric data by phenology rather than topography

33. Discussion Don Atwood dkatwood@alaska.edu (907) 474-7380 Photo Credit: Don Atwood