Spectral-spatial hyperspectral image classification via superpixel merging and sparse representation

Recently, the superpixel segmentation is introduced into the hyperspectral image (HSI) classification to exploit the spatial information. However, the size of superpixels influences the classification significantly because small superpixels can not provide enough spatial information and large superpixels generally result in error segmentation. The error segmentation is irreversible and intolerable, so the size of superpixels tends to be small. This paper proposes a hyperspectral unmixing based superpixel merging criterion to merge small su-perpixels and thus make use of the spatial information. The spatial information is then incorporated into the joint sparsity model for the spectral-spatial classification. Experimental results demonstrate the superiority of the proposed method over some widely used classification methods.