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Spectral unmixing (SU) of hyperspectral images (HSIs) is one of the importantareas in remote sensing (RS) that needs to be carefully addressed in differentRS applications. Despite the high spectral resolution of the hyperspectraldata, the relatively low spatial resolution of the sensors may lead to mixtureof different pure materials within the image pixels. In this case, the spectrumof a given pixel recorded by the sensor can be a combination of multiplespectra each belonging to a unique material in that pixel. Spectral unmixing isthen used as a technique to extract the spectral characteristics of thedifferent materials within the mixed pixels and to recover the spectrum of eachpure spectral signature, called endmember. Block-sparsity exists inhyperspectral images as a result of spectral similarity between neighboringpixels. In block-sparse signals, the nonzero samples occur in clusters and thepattern of the clusters is often supposed to be unavailable as priorinformation. This paper presents an innovative spectral unmixing approach forHSIs based on block-sparse structure. Hyperspectral unmixing problem is solvedusing pattern coupled sparse Bayesian learning strategy (PCSBL). To evaluatethe performance of the proposed SU algorithm, it is tested on both syntheticand real hyperspectral data and the quantitative results are compared to thoseof other state-of-the-art methods in terms of abundance angle distance and meansquared error. The achieved results show the superiority of the proposedalgorithm over the other competing methods by a significant margin.

Spectral Unmixing of Hyperspectral Images Based on Block Sparse Structure