Sea ice monitoring using spaceborne multi-polarization and polarimetric SAR imagery

• Main Author: Scheuchl, Bernd
• Format: Thesis
• Language: English
• Published: 2006
• Subjects: Asar; Sea ice
• Online Access: http://hdl.handle.net/2429/18410

The imminent launch of RADARSAT-2, the most advanced of the second-generation spaceborne SARs, has stimulated renewed interest in polarization diversity for sea ice monitoring. The primary objective of this work is to assess the potential value of RADARSAT-2 multi-polarization and polarimetric C-band SAR imagery for classification of sea ice and to develop improved classifiers that account for the characteristics of such imagery. Our review of the ice information requirements of the Canadian Ice Service reveals the importance of daily revisit for operations. The need to determine the ice edge location, the ice concentration, and stage of development of ice can be addressed by accurate classification of ice types in SAP, imagery. Our application of target decomposition to AIRSAR airborne polarimetric imagery of sea ice reveals that surface scattering dominates the majority of the scene. Pixel by pixel application of target decomposition methods can be used to distinguish thin ice, first-year ice, and multi-year ice with some success. When classifying sea ice, we show that a recently proposed K-means clustering algorithm which uses a Wishart classifier can be substantially simplified by initializing it with a seed based solely on backscatter levels. Our analysis of AIRSAR airborne polarimetric imagery of sea ice suggests that classification accuracy obtained using dual-polarization imagery is similar to that of polarimetric imagery and better than that of single-polarization imagery. Our analysis of simulated RADARSAT-2 polarimetric imagery derived from airborne CV-580 imagery indicates that speckle noise degrades our ability to distinguish between ice types more than the increase in NESZ but can easily be reduced through spatial filtering. In simulated dual-polarization ScanSAR imagery, open water and sea ice can be easily distinguished by using both co- and cross-polarized image in spite of the high NESZ level. In our analysis of ENVISAT ASAR AP imagery that covers a full ice season, multi-year ice could be distinguished from other types in eight out of ten scenes available. When antenna pattern correction causes a variation of NESZ over the swath, we show that adaptive classification scheme can compensate for such variation. Applied Science, Faculty of Electrical and Computer Engineering, Department of Graduate