Superpixel-guided multiscale kernel collaborative representation for hyperspectral image classification

Abstract

This article presents a superpixel-guided multiscale kernel collaborative representation method for robust classification of hyperspectral images. This novel method first exploits the spatial multiscale information of hyperspectral images by extending a superpixel segmentation algorithm, and then proposes a spatial-spectral information fusion technique to encode the spatial multiscale similarities and the spectral similarities between the pixels in the framework of kernel collaborative representation classification. The advantages of it mainly consist in (1) avoiding choosing empirical parameters in the spatial feature extraction process of superpixels and (2) enhanced classification accuracy as compared to traditional spatial-spectral kernel techniques. Experimental results with two widely used hyperspectral images demonstrate the effectiveness of the proposed method.