Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for spectral unmixing. However, such methods fail to fully exploit the potential of topic models in uncovering image semantics, and they need extra sparsity constraints, which greatly increases the complexity of the model. To solve these problems, a sparse topic relaxion and group clustering model for HU (STRGC) is proposed. In STRGC, the sparse prior constraints implied by the sparse topic model are introduced, which means that the sparse characteristics of the model are used to capture the semantic representation of the spectrum. Through the relaxation of the model, the possible spectral representations of ground features can be obtained, and this further alleviates the influence caused by endmember variability on the accuracy of the unmixing process. Then, fuzzy clustering is used to locate the position of the endmember quickly and accurately. Furthermore, unmixing models with different characteristics are united to alleviate the ill-posed nature of the model, thereby improving the fractional abundance. Experimental results obtained with one simulated dataset and three well-known real hyperspectral datasets confirm the effectiveness and advantages of the proposed method.

中文翻译：

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高光谱分解的稀疏主题松弛和群聚模型

高光谱分解（HU）一直是高光谱遥感领域的研究热点。近年来，采用概率主题模型来获取高光谱图像的潜在主题已成为一种有效的光谱分解方法。但是，这些方法无法充分利用主题模型在揭示图像语义方面的潜力，并且需要额外的稀疏性约束，这极大地增加了模型的复杂性。为了解决这些问题，提出了一种针对HU的稀疏主题松弛和群组聚类模型（STRGC）。在STRGC中，引入了稀疏主题模型所隐含的稀疏先验约束，这意味着该模型的稀疏特征被用来捕获频谱的语义表示。通过放松模型，可以获得地面特征的可能的频谱表示，这进一步减轻了端构件可变性对拆解过程的准确性造成的影响。然后，使用模糊聚类快速而准确地定位末端构件的位置。此外，将具有不同特征的解混模型组合在一起以减轻模型的不适定性，从而提高分数丰度。使用一个模拟数据集和三个著名的真实高光谱数据集获得的实验结果证实了该方法的有效性和优势。模糊聚类用于快速，准确地定位末端构件的位置。此外，将具有不同特征的解混模型组合在一起以减轻模型的不适定性，从而提高分数丰度。使用一个模拟数据集和三个著名的真实高光谱数据集获得的实验结果证实了该方法的有效性和优势。模糊聚类用于快速，准确地定位末端构件的位置。此外，将具有不同特征的解混模型组合在一起以减轻模型的不适定性，从而提高分数丰度。使用一个模拟数据集和三个著名的真实高光谱数据集获得的实验结果证实了该方法的有效性和优势。