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Unsupervised Classification of Sea-Ice and Water Using Synthetic Aperture Radar via an Adaptive Texture Sparsifying Transform

TitleUnsupervised Classification of Sea-Ice and Water Using Synthetic Aperture Radar via an Adaptive Texture Sparsifying Transform
Publication TypeConference Paper
Year of Publication2013
AuthorsAmelard, R., A. Wong, D. A. Clausi, and F. Li
Conference NameIEEE International Geoscience and Remote Sensing Symposium
Keywordssea-ice classification, sparsifying transform, synthetic aperture radar, texture model
Abstract

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.