Due to the limited spatial resolution of hyperspectral sensors, each pixel in hyperspectral image often consists of several components, called endmembers. Hyperspectral unmixing aims at extracting these endmembers and corresponding fractional abundances from the hyperspectral image (HSI) data. With the availability of spectral libraries, semi-supervised unmixing which estimates the abundance from given endmember matrix, have become more and more popular. General semi-supervised methods take advantage of the sparsity constraint on the abundance matrix and consider the pixels as independent trials. However, the spatial information for example the correlation between pixels often cannot be taken into consideration. In this paper, we derive a semi-supervised hyperspectral image unmixing algorithm which handles both spectral and spatial prior efficiently using matrix factorization. The abundance matrix is recast as a multiplication of two variables, in which the spectral and spatial priors are captured respectively. Numerical tests in both simulated and real datasets show that compared to state-of-the-art unmixing algorithms, the proposed spectral-spatial factorization method has lower computation cost, better unmixing results, and is more robust to regularization parameter selection.

中文翻译：

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通过光谱空间分解对高光谱数据进行半监督分解


由于高光谱传感器的空间分辨率有限，高光谱图像中的每个像素通常由多个分量组成，称为端元。高光谱分解旨在从高光谱图像（HSI）数据中提取这些端元和相应的分数丰度。随着光谱库的可用性，半监督分解（根据给定端元矩阵估计丰度）变得越来越流行。一般的半监督方法利用丰度矩阵的稀疏性约束，并将像素视为独立的试验。然而，空间信息，例如像素之间的相关性，往往无法被考虑在内。在本文中，我们推导了一种半监督高光谱图像混合算法，该算法使用矩阵分解有效地处理光谱和空间先验。丰度矩阵被重新构建为两个变量的乘法，其中分别捕获光谱和空间先验。模拟和真实数据集中的数值测试表明，与最先进的解混合算法相比，所提出的谱空间分解方法具有更低的计算成本、更好的解混合结果，并且对正则化参数选择更鲁棒。