Extracting high discrimination and shift invariance features in synthetic aperture radar images

Abstract

A feature processing technique that is specifically designed for synthetic aperture radar images, where features are extracted with merits of compactness, high discrimination ability, and shift invariance is proposed. To this end, a rich feature set is first constructed with a wealth of discrimination information. Then, the redundancy and dimensionality of the rich feature set are reduced such that a much more compact and efficient feature set can be achieved. Finally, the advantages of the compact feature set are further explored by learning the relationships among features statistically in discriminative fashion. Experimental results show the efficiency of the proposed method.