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Spectral-Spatial Sparse Subspace Clustering Based on Three-Dimensional Edge-Preserving Filtering for Hyperspectral Image
Li A.3; Qin A.3; Hu S.1; Shang Z.3; Tang Y.Y.2
2018-11-07
Source PublicationProceedings - International Conference on Machine Learning and Cybernetics
Volume1
Pages167-172
AbstractDue to the 3-D property of raw HSI cubes, 3-D spectral-spatial filter becomes an effective way for extracting spectral and spatial signatures from HSI. In this paper, a new spectral-spatial sparse subspace clustering framework based on 3-D edge-preserving filtering is proposed to improve the clustering accuracy of HSI. First, the initial sparse coefficient matrix is obtained in the s-parse representation process of the classical SSC model. Then, a 3-D edge-preserving filtering is conducted on the initial sparse coefficient matrix to get a more accurate one, which is used to build the similarity graph. Finally, the clustering result of H-SI data is achieved by employing the spectral clustering algorithm to the similarity graph. Specifically, the filtered matrix can not only capture the spectral-spatial features but the intensity differences. Experimental results demonstrate the potential of including the proposed 3-D edge-preserving filtering into the SSC framework can improve the clustering accuracy.
Keyword3-D edge-preserving filters (3-D EPFs) Hyperspectral images (HSIs) Intensity differences Sparse subspace clustering (SSC)
DOI10.1109/ICMLC.2018.8527015
URLView the original
Language英語
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Qingyang People's Hospital Qingyang
2.Universidade de Macau
3.Chongqing University
Recommended Citation
GB/T 7714
Li A.,Qin A.,Hu S.,et al. Spectral-Spatial Sparse Subspace Clustering Based on Three-Dimensional Edge-Preserving Filtering for Hyperspectral Image[C],2018:167-172.
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