Unsupervised classification of sea-ice using synthetic aperture radar via an adaptive texture sparsifying transform

A texture sparsifying transform for use in unsupervised classification of sea-ice in polarimetric synthetic aperture radar (SAR) imagery is presented. The goal of the sparsifying transform is to compactly represent the underlying information of the SAR imagery to eliminate sources of unwanted noise and complexities (e.g., banding effect on RADARSAT-2) commonly found in SAR imagery. The proposed algorithm is designed to be simple to implement and discriminative in sea-ice scenes. Performing unsupervised classification on the sparsifying transform space using scenes captured with C-band HV polarization yields experimental results that are much more accurate than common pixel-based methods, and performs comparably to a recent more complex method.