ABSTRACT In hyperspectral applications, spectral unmixing (SU) is an important technology to obtain the endmembers and the fractional land covers. Spectral variability, outliers, and nonlinearity are three challenging issues, causing SU to extract endmembers and corresponding abundance maps inaccurately. However, in view of the complexity of the three issues, to tackle them at once is difficult and intractable. In this paper, all the aforementioned issues are advocated to process together by a powerful integrative SU framework, where a hyper-manifold learning approach via a sparsity-constrained dual is exploited. In the proposed integrative SU framework, heterogeneous and homogeneous information is explored by dividing the hyperspectral data (HD) into a series of sub-blocks, hyper-Laplacian-based graph is employed to address the nonlinearity, and spectral variability is controlled by a scaled matrix. Moreover, the interferences of the outliers are handled by the augmented correntropy-induced metric (ACIM), where the rare endmembers are separated from the abrupt anomalies via decomposing the HD sets in a low-rank structure and constraining the sparsity on the corresponding residual term. The experimental results on several popularly used real HD sets indicate the superior performances of the proposed approach.

中文翻译：

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通过集成框架进行高光谱亚像素解混

摘要 在高光谱应用中，光谱分离（SU）是获得端元和部分土地覆盖的重要技术。光谱变异性、异常值和非线性是三个具有挑战性的问题，导致 SU 提取端元和相应的丰度图不准确。但是，鉴于这三个问题的复杂性，要​​一蹴而就，难度不小。在本文中，所有上述问题都提倡通过强大的集成 SU 框架一起处理，其中利用了通过稀疏约束对偶的超流形学习方法。在提出的集成 SU 框架中，通过将高光谱数据 (HD) 划分为一系列子块来探索异构和同质信息，并采用基于超拉普拉斯算子的图来解决非线性问题，光谱可变性由缩放矩阵控制。此外，异常值的干扰由增强相关熵诱导度量（ACIM）处理，其中通过分解低秩结构中的 HD 集并约束相应残差项的稀疏性，将稀有端元与突变异常分开. 几个常用的真实高清集的实验结果表明所提出的方法的优越性能。