融合高光谱影像三维空谱特征的子空间聚类算法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》

李文洲 邓秀勤 刘富春

Abstract： In order to improve the clustering accuracy of hyperspectral images, this paper proposed a new sparse subspace clustering model combined three-dimensional spatial spectral features with subspace clustering algorithms. While focusing on the spectral information of hyperspectral images, it also paid attention to spatial context information. Firstly, extracting three kinds of three-dimensional spatial spectral features from the pixels of the hyperspectral image. Then the features weighted the coefficient matrix of the subspace clustering model so that the pixel points could sparsely represent the pixel point to which they are most similar, thereby obtaining the better coefficient matrix. Finally, it used the coefficient matrix to obtain better clustering results with spectral clustering. The algorithm experimented on four classical hyperspectral datasets, and compared the experimental results with six clustering algorithms. The results show that the proposed algorithm achieves higher clustering accuracy on the four datasets than the other algorithms. The algorithm can achieve at most 8.62% accuracy than the algorithms based 3D spatial spectral features like M3DF^3 algotithm and 3DF-SSC algorithm, and at most 25.18% than the algorithms which improve the subspace clustering algorithm by using spatial context information like L2-SSC algorithm and SS-LRSC algorithm.

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