Region-Based Relaxed Multiple Kernel Collaborative Representation for Hyperspectral Image Classification

This paper presents a region-based relaxed multiple kernel collaborative representation method for the spatial-spectral classification of hyperspectral images. The proposed method consists of three steps. In the first step, a multiscale method achieved by extending a superpixel segmentation algorithm is designed to capture the spatial-spectral information of hyperspectral images. For each scale, a hyperspectral image can be segmented into several nonoverlapping spectrally similar regions that consist of some spatially adjacent pixels. In the second step, two criteria (i.e., the first two moments) are computed within the regions of each scale to generate the corresponding spatial features. In the final step, a relaxed multiple kernel technique is proposed to fuse the obtained spatial multiscale features and original spectral features in the framework of column generation kernel collaborative representation classification. Experimental results obtained from two real hyperspectral images demonstrate the effectiveness of the proposed method as compared with some popular spatial-spectral techniques.

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