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An unsupervised classification approach for hyperspectral images based adaptive spatial and spectral neighborhood selection and graph clustering

机译：基于自适应空间和光谱邻域选择以及图聚类的高光谱图像无监督分类方法

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摘要

In remote sensing image processing, the classification is an interesting step to distinguish the image scene composition and can be of interesting role in different applications such as environmental monitoring and geological studies. Unlike the clustering, the classification needs labeled data for the training; however gaining these labeled data was always an expensive and hard task. For that, in this paper we propose an unsupervised classification approach that gains its labeled data from the proposed spatial and spectral graph clustering approach. The proposed adaptive spatial and spectral neighborhood selection approach is an extension of the k nearest neighborhood that assigns an adaptive number of neighbors to each pixel depending in its spatial and spectral relationship to its neighboring pixels. Then, this neighborhood will be clustered, to provide the first labeled training set, based on a hierarchical graph clustering algorithm. Finally, an SVM classification with a recursive kernel will be performed on the selected first labeled data at a first step and then the classification results are improved with the classification iterations of the recursive kernel. Experimental results on real hyperspectral images proved that with few iterations of the recursive kernel the proposed approach results are similar and even better then the supervised classification.
机译：在遥感图像处理中，分类是区分图像场景组成的有趣步骤，并且在不同的应用（例如环境监测和地质研究）中可以发挥有趣的作用。与聚类不同，分类需要标签数据进行训练。但是，获得这些标记的数据始终是一项昂贵而艰巨的任务。为此，在本文中，我们提出了一种无监督分类方法，该方法从提出的空间和频谱图聚类方法中获取其标记数据。所提出的自适应空间和频谱邻域选择方法是k个最近邻域的扩展，它根据每个像素与其相邻像素的空间和频谱关系为每个像素分配了自适应的邻居数。然后，将基于层次图聚类算法对该邻域进行聚类，以提供第一个标记的训练集。最后，第一步将对选定的第一个标记数据执行带有递归内核的SVM分类，然后通过递归内核的分类迭代改进分类结果。在真实的高光谱图像上的实验结果证明，在递归内核很少迭代的情况下，所提出的方法结果是相似的，甚至比监督分类更好。

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