Spectral unmixing is an important problem for remotely sensed hyperspectral data exploitation. Automatic spectral unmixing can be viewed as a three-stage problem, where the first stage is subspace identification, the next one is endmember extraction, and the final one is abundance estimation. In this sequence, endmember extraction is the most challenging problem. Many researchers have attempted to extract endmembers from hyperspectral images using spectral information only. However, it is well known that the inclusion of spatial information can improve the endmember extraction task. In this article, we introduce a new endmember extraction algorithm that exploits both spectral and spatial information. A main innovation of the proposed algorithm is that spatial information is exploited using entropy, while spectral information is exploited using convex set optimization. In the literature, none of the spatial–spectral algorithms has used entropy as spatial information. The inclusion of this entropy-based spatial information improves the accuracy of the endmember extraction process. The results obtained by the proposed algorithm are compared (using a variety of metrics) with those obtained by other state-of-the-art methods, using both synthetic and real datasets. Our experimental results demonstrate that the proposed algorithm outperforms many available algorithms.

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基于熵的凸集优化用于从高光谱图像中提取空间光谱端元

光谱分离是遥感高光谱数据开发的一个重要问题。自动光谱解混可以看作是一个三阶段问题，第一阶段是子空间识别，下一个阶段是端元提取，最后一个阶段是丰度估计。在这个序列中，端元提取是最具挑战性的问题。许多研究人员试图仅使用光谱信息从高光谱图像中提取端元。然而，众所周知，包含空间信息可以改进端元提取任务。在本文中，我们介绍了一种新的端元提取算法，它利用了光谱和空间信息。所提出算法的一个主要创新是使用熵来利用空间信息，而光谱信息是使用凸集优化来利用的。在文献中，没有一个空间谱算法使用熵作为空间信息。包含这种基于熵的空间信息提高了端元提取过程的准确性。将所提出的算法获得的结果（使用各种度量）与其他最先进的方法获得的结果进行比较，使用合成数据集和真实数据集。我们的实验结果表明，所提出的算法优于许多可用的算法。将所提出的算法获得的结果（使用各种度量）与其他最先进的方法获得的结果进行比较，使用合成数据集和真实数据集。我们的实验结果表明，所提出的算法优于许多可用的算法。将所提出的算法获得的结果（使用各种度量）与其他最先进的方法获得的结果进行比较，使用合成数据集和真实数据集。我们的实验结果表明，所提出的算法优于许多可用的算法。