Semisupervised Discriminant Feature Learning for SAR Image Category via Sparse Ensemble

Abstract

Terrain scene classification plays an important role in various synthetic aperture radar (SAR) image understanding and interpretation. This paper presents a novel approach to characterize SAR image content by addressing category with a limited number of labeled samples. In the proposed approach, each SAR image patch is characterize by a discriminant feature which is generated in a semisupervised manner by utilizing a spare ensemble learning procedure. In particular, a nonnegative sparse coding procedure is applied on the given SAR image patch set to generate the feature descriptors first. The set is combined with a limited number of labeled SAR image patches and an abundant number of unlabeled ones. Then, a semisupervised sampling approach is proposed to construct a set of weak learners, in which each one is modeled by a logistic regression procedure. The discriminant information can be introduced by projecting SAR image patch on each weak learner. Finally, the features of SAR image patches are produced by a sparse ensemble procedure which can reduce the redundancy of multiple weak learners. Experimental results show that the proposed discriminant feature learning approach can achieve a higher classification accuracy than several state-of-the-art approaches.