Spectral-spatial classification of hyperspectral images via multiscale superpixels based sparse representation

Abstract

Recently, the superpixel segmentation is introduced into the hyperspectral image (HSI) classification to exploit the spatial information. However, the size of superpixel is hard to determine since small superpixels lack enough spatial information and large superpixels usually result in error segmentation. Therefore, a multiscale superpixels based sparse representation (MSSR) algorithm is proposed to utilize the spatial-spectral information of multiscale superpixels for the HSI classification. Specifically, multiscale superpixels of a HSI are generated firstly. Then, the joint sparse representation classification (JSRC) is used to obtain the class labels of superpixels of different scales. Finally, the majority voting is applied on the labels of different scales to create the final class label for each pixel. Experimental results show that the proposed MSSR algorithm outperforms several well-known classification algorithms.