Superpixel-Based Hyperspectral Unmixing with Regional Segmentation

Abstract

In this paper, a superpixel-based hyperspectral image unmixing is proposed. First, superpixel segmentation is applied to the image. A low dimensional representation of the image is created by representing each superpixel by its mean spectra. Second, the superpixel image is segmented into regions. Endmember extraction is applied to each region to extract local endmembers. Abundances are computed over the full image using the extracted endmembers. The approach is compared with the global unmixing and the local unmixing using the original image. Experimental results are presented using the HYDICE Urban hyperspectral image.