ABSTRACT Spectral variability existing in hyperspectral (HS) images reduces the accuracy of unmixing. To mitigate the effect of spectral variability on unmixing, we propose a spatial-spectral clustering-based method (called SP2C) for endmember extraction and HS unmixing. The proposed SP2C adopts a local spatial-spectral clustering strategy to obtain a set of spatially homogenous regions and exploits a spectral purity index calculation strategy to choose pure pixels on the regions, where the average spectra of chosen pure pixels on the regions are taken as endmember candidates to alleviate local spectral variability. Then, an adjusted k-means plus (called AD k-means++) algorithm is employed to cluster candidate endmembers to alleviate global spectral variability, and the final endmembers are achieved by matching the cluster centers and the reference endmembers obtained by the vertex component analysis (VCA)-like methods. Our experiment results, conducted using real HS datasets, confirm that the proposed method considerably improves the HS unmixing performance compared to the state-of-the-art techniques.

中文翻译：

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一种基于空间光谱聚类的端元提取和高光谱解混算法

摘要 高光谱 (HS) 图像中存在的光谱可变性降低了解混的准确性。为了减轻光谱可变性对解混的影响，我们提出了一种基于空间光谱聚类的方法（称为 SP2C）用于端元提取和 HS 解混。提出的SP2C采用局部空间光谱聚类策略获得一组空间同质区域，并利用光谱纯度指数计算策略选择区域上的纯像素，其中区域上所选纯像素的平均光谱作为端元减轻局部光谱可变性的候选者。然后，采用调整后的 k-means plus（称为 AD k-means++）算法对候选端元进行聚类，以减轻全局光谱可变性，最终的端元是通过匹配聚类中心和通过顶点分量分析（VCA）类方法获得的参考端元来实现的。我们使用真实 HS 数据集进行的实验结果证实，与最先进的技术相比，所提出的方法显着提高了 HS 解混性能。