Hyperspectral unmixing (HU) is a crucial technique for exploiting remotely sensed hyperspectral data, which aims at estimating a set of spectral signatures, called endmembers and their corresponding proportions, called abundances. The performance of HU is often seriously degraded by various kinds of noise existing in hyperspectral images (HSIs). Most of existing robust HU methods are based on the assumption that noise or outlier only exists in one kind of formulation, e.g., band noise or pixel noise. However, in real-world applications, HSIs are unavoidably corrupted by noisy bands and noisy pixels simultaneously, which require robust HU in both the spatial dimension and spectral dimension. Meanwhile, the sparsity of abundances is an inherent property of HSIs and different regions in an HSI may possess various sparsity levels across locations. This article proposes a correntropy-based spatial-spectral robust sparsity-regularized unmixing model to achieve 2-D robustness and adaptive weighted sparsity constraint for abundances simultaneously. The updated rules of the proposed model are efficient to be implemented and carried out by a half-quadratic technique. The experimental results obtained by both synthetic and real hyperspectral data demonstrate the superiority of the proposed method compared to the state-of-the-art methods.

中文翻译：

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基于相关熵的空间光谱鲁棒稀疏正则化高光谱解混

高光谱分离 (HU) 是利用遥感高光谱数据的一项关键技术，其目的是估计一组光谱特征，称为端元及其相应的比例，称为丰度。高光谱图像（HSI）中存在的各种噪声通常会严重降低 HU 的性能。大多数现有的鲁棒 HU 方法都基于这样的假设，即噪声或异常值仅存在于一种公式中，例如带状噪声或像素噪声。然而，在实际应用中，HSI 不可避免地同时被噪声带和噪声像素破坏，这需要在空间维度和光谱维度上都具有稳健的 HU。同时，丰度的稀疏性是 HSI 的固有属性，HSI 中的不同区域可能在不同位置具有不同的稀疏程度。本文提出了一种基于相关熵的空间谱鲁棒稀疏正则化解混合模型，以同时实现对丰度的二维鲁棒性和自适应加权稀疏约束。所提出模型的更新规则可以通过半二次技术有效地实施和执行。通过合成和真实高光谱数据获得的实验结果证明了所提出的方法与最先进的方法相比的优越性。