Semi-supervised sparse relearning representation classification for high-resolution remote sensing imagery

Abstract

In this article, we proposed a novel semi-supervised sparse representation classification for high resolution remote sensing image. First, collaborative representation mechanism that exploits the help from whole training information rather than from only the potential associated class can enhance the class recognition ability. Second, by taking advantage of spatial occurrence and alignment of class label, the adoption of the relearning can gradually learn the flexible class-oriented spatial pattern from the label space with alleviated computational complexity to enhance the original spectral characteristics. Third, inspired by the spatial smoothing phenomenon when spatial feature stacked, a novel stable self-learning method can be designed to automatically select informative unlabeled sample to help the limited supervised set. Experiments on two hyperspectral and high-spatial resolution images validated the effectiveness and robustness of the proposed algorithm.