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Spectral clustering eigenvector selection of hyperspectral image based on the coincidence degree of data distribution

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International Journal of Digital Earth 2023年第1期16卷 3489-3512页

作者： Zhongliang Ren Qiuping Zhai Lin Sun College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdaoPeople’s Republic of China Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection School of Resource and Environmental SciencesLinyi UniversityPeople’s Republic of China

Spectral clustering is a well-regarded subspace clustering algorithm that exhibits outstanding performance in hyperspectral image classification through eigenvalue decomposition of the Laplacian ***,its classification accuracy is severely limited by the selected eigenvectors,and the commonly used eigenvectors not only fail to guarantee the inclusion of detailed discriminative information,but also have high computational *** address these challenges,we proposed an intuitive eigenvector selection method based on the coincidence degree of data distribution(CDES).First,the clustering result of improved k-means,which can well reflect the spatial distribution of various types was used as the reference ***,the adjusted Rand index and adjusted mutual information were calculated to assess the data distribution consistency between each eigenvector and the reference ***,the eigenvectors with high coincidence degrees were selected for clustering.A case study on hyperspectral mineral mapping demonstrated that the mapping accuracies of CDES are approximately 56.3%,15.5%,and 10.5%higher than those of the commonly used top,high entropy,and high relevance eigenvectors,and CDES can save more than 99%of the eigenvector selection ***,due to the unsupervised nature of k-means,CDES provides a novel solution for autonomous feature selection of hyperspectral images.

关键词： Eigenvector selection spectral clustering coincidence degree of data distribution hyperspectral mineral mapping

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