Classification of fully polarimetric single- and dual-frequency SAR data of sea ice using the Wishart statistics

Abstract

Information on the extent and composition of sea ice is important for shipping and offshore operations. Single-polarization spaceborne synthetic aperture radar (SAR) data are an important information source for ice centres around the world. Next-generation SAR satellites will have the capability to collect fully polarimetric SAR data. In this paper we analyze the differences between polarimetric signatures of sea ice and those of land, test several applications of an unsupervised segmentation based on the Wishart distribution of polarimetric data, and give recommendations for modifications to the segmentation method. Airborne C- and L-band sea ice data are investigated. Surface scattering largely dominates sea ice backscatter. A volume component is present; it only dominates for some regions of selected ice types depending on the radar frequency. Dihedral scattering rarely dominates. The application of speckle filters further reduces the number of pixels with predominantly double bounce. The separation of scattering characteristics using the Freeman–Durden model before image classification provides little additional information. The initialization of the segmentation method with a scattering strength based classifier is very efficient and leads to good results.