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.

翻译：高光谱图像的光谱解混是遥感领域中需要在不同遥感应用中仔细处理的重要方向之一。尽管高光谱数据具有高光谱分辨率，但传感器相对较低的空间分辨率可能导致图像像素内不同纯物质的混合。此时，传感器记录给定像素的光谱可能是该像素内多种不同物质光谱的组合。光谱解混作为一种技术，用于提取混合像素内不同物质的光谱特征，并恢复每种纯光谱特征（称为端元）的光谱。由于相邻像素间的光谱相似性，高光谱图像中普遍存在块稀疏性。在块稀疏信号中，非零样本成簇出现，且簇的模式通常被视为未知先验信息。本文提出一种基于块稀疏结构的创新高光谱图像光谱解混方法。利用模式耦合稀疏贝叶斯学习策略解决高光谱解混问题。为评估所提光谱解混算法的性能，在合成和真实高光谱数据上进行了测试，并与其它最先进方法在丰度角距离和均方误差方面的定量结果进行了比较。实验结果表明，所提算法显著优于其他竞争方法。