Spectral Unmixing of Hyperspectral Images Based on Block Sparse Structure

TL;DR

This paper introduces a novel spectral unmixing method for hyperspectral images leveraging block-sparse structures and pattern coupled sparse Bayesian learning, demonstrating superior accuracy on synthetic and real data.

Contribution

It proposes a new spectral unmixing approach based on block-sparsity and sparse Bayesian learning, improving accuracy over existing methods.

Findings

Outperforms state-of-the-art methods in accuracy

Effective on both synthetic and real hyperspectral data

Significant reduction in abundance angle distance and mean squared error

Abstract

Spectral unmixing (SU) of hyperspectral images (HSIs) is one of the important areas in remote sensing (RS) that needs to be carefully addressed in different RS applications. Despite the high spectral resolution of the hyperspectral data, the relatively low spatial resolution of the sensors may lead to mixture of different pure materials within the image pixels. In this case, the spectrum of a given pixel recorded by the sensor can be a combination of multiple spectra each belonging to a unique material in that pixel. Spectral unmixing is then used as a technique to extract the spectral characteristics of the different materials within the mixed pixels and to recover the spectrum of each pure spectral signature, called endmember. Block-sparsity exists in hyperspectral images as a result of spectral similarity between neighboring pixels. In block-sparse signals, the nonzero samples occur in clusters and the pattern of the clusters is often supposed to be unavailable as prior information. This paper presents an innovative spectral unmixing approach for HSIs based on block-sparse structure. Hyperspectral unmixing problem is solved using pattern coupled sparse Bayesian learning strategy (PCSBL). To evaluate the performance of the proposed SU algorithm, it is tested on both synthetic and real hyperspectral data and the quantitative results are compared to those of other state-of-the-art methods in terms of abundance angle distance and mean squared error. The achieved results show the superiority of the proposed algorithm over the other competing methods by a significant margin.