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Band selection of hyperspectral image by sparse manifold clustering

机译：稀疏歧管聚类的Hyperspectral图像的频段选择

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摘要

Band selection of hyperspectral images is an optimal feature selection method, which aims at reducing the computational burden associated with processing the whole data. The significant and informative bands identified by the band selection process lead to efficient, compact representation of the image data and produce a satisfactory performance in the succeeding applications viz. classification, unmixing, target detection and so on. In this study, the authors present an unsupervised manifold clustering approach for band selection, which accounts for different types of scenarios. Unlike other band selection approaches, the authors' proposed manifold clustering framework identifies the informative bands by utilising the interrelation between the bands and accounts for the multi-manifold structure prevalent in some real images. The proposed band selection framework identifies the optimal number of clusters by cluster validity index, clusters the bands by manifold clustering and select representative bands from each cluster according to graph weight. Their proposed manifold clustering approach is a generic clustering approach, which produces a satisfactory result even when the data contains non-linearity. The information theoretic performance measures, classification and unmixing performance on real image experiments demonstrate the proficiency of their proposed band selection algorithm.
机译：频带选择高光谱图像是最佳特征选择方法，其旨在减少与处理整个数据相关的计算负担。频带选择过程识别的重要频带导致图像数据的高效，紧凑的表示，并在后续应用VIZ中产生令人满意的性能。分类，解密，目标检测等。在这项研究中，作者呈现了一个无人驾驶的歧管聚类方法，用于频带选择，该方法占不同类型的情景。与其他频带选择方法不同，作者提出的歧管聚类框架通过利用频带之间的相互关系来识别信息频带，并且在某些真实图像中普遍的多歧管结构占用。所提出的频带选择框架通过集群有效性索引识别群集的最佳簇数，通过歧管聚类群集频带，并根据图重量选择来自每个群集的代表频带。它们所提出的歧管聚类方法是一种通用聚类方法，即使当数据包含非线性时，也产生令人满意的结果。真实图像实验中的信息理论性能措施，分类和解密性能证明了其提出的频段选择算法的熟练程度。

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