Sparse unmixing, as a semisupervised unmixing method, has attracted extensive attention. The process of sparse unmixing involves treating the mixed pixels of hyperspectral imagery as a linear combination of a small number of spectral signatures (endmembers) in a standard spectral library, associated with fractional abundances. Over the past ten years, to achieve a better performance, sparse unmixing algorithms have begun to focus on the spatial information of hyperspectral images. However, less accurate spatial information greatly limits the performance of the spatial-regularization-based sparse unmixing algorithms. In this article, to overcome this limitation and obtain more reliable spatial information, a novel sparse unmixing algorithm named superpixel-based reweighted low-rank and total variation (SUSRLR-TV) is proposed to enhance the performance of the traditional spatial-regularization-based sparse unmixing approaches. In the proposed approach, superpixel segmentation is adopted to consider both the spatial proximity and the spectral similarity. In addition, a low-rank constraint is enforced on the objective function as pixels within each superpixel have the same endmembers and similar abundance values, and they naturally satisfy the low-rank constraint. Differing from the traditional nuclear norm, a reweighted nuclear norm is used to achieve a more efficient and accurate low-rank constraint. Meanwhile, low-rank consideration is also used to enhance the spatial continuity and suppress the effects of random noise. Furthermore, TV regularization is introduced to promote the smoothness of the abundance maps. Experiments on three simulated data sets, as well as a well-known real hyperspectral imagery data set, confirm the superior performance of the proposed method in both the qualitative assessment and the quantitative evaluation, compared with the state-of-the-art sparse unmixing methods.

中文翻译：

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基于超像素的加权低秩和总变化稀疏分解，用于高光谱遥感影像

稀疏分解作为一种半监督分解方法已引起广泛关注。稀疏分解的过程涉及将高光谱图像的混合像素视为标准光谱库中少量光谱特征（端成员）的线性组合，并与分数丰度关联。在过去的十年中，为了获得更好的性能，稀疏分解算法已开始关注高光谱图像的空间信息。但是，精度较低的空间信息极大地限制了基于空间正则化的稀疏分解算法的性能。在本文中，为了克服此限制并获得更可靠的空间信息，为了提高传统的基于空间正则化的稀疏分解方法的性能，提出了一种新的稀疏分解算法，即基于超像素的加权低秩和总方差（SUSRLR-TV）。在提出的方法中，采用超像素分割来考虑空间邻近性和光谱相似性。另外，由于每个超像素内的像素具有相同的端成员和相似的丰度值，因此对目标函数强制实施低秩约束，并且自然满足低秩约束。与传统的核规范不同，重新加权的核规范用于实现更有效和准确的低阶约束。同时，低等级考虑还用于增强空间连续性并抑制随机噪声的影响。此外，引入电视正则化以提高丰度图的平滑度。通过对三个模拟数据集以及一个著名的真实高光谱图像数据集进行的实验，证实了与最新的稀疏分解相比，该方法在定性评估和定量评估方面均具有出色的性能。方法。