Sparsity-Based Clustering for Large Hyperspectral Remote Sensing Images
Hyperspectral image (HSI) clustering is extremely challenging because of the complexity of the image structure. Recently, the subspace clustering algorithms have achieved competitive performance for HSIs. However, these methods generally are computationally complex and time-and-memory-consuming, giv...
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| Published in: | IEEE transactions on geoscience and remote sensing Vol. 59; no. 12; pp. 10410 - 10424 |
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| Main Authors: | , , , |
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| Language: | English |
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01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0196-2892, 1558-0644 |
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| Abstract | Hyperspectral image (HSI) clustering is extremely challenging because of the complexity of the image structure. Recently, the subspace clustering algorithms have achieved competitive performance for HSIs. However, these methods generally are computationally complex and time-and-memory-consuming, given their reliance on large-scale adjacency matrix learning and graph segmentation, which limits their application to large HSIs and reduces their attractiveness in real applications. In this article, in view of this, two novel sparsity-based clustering algorithms are proposed for large HSIs, named sparse coding-based clustering (SCC) and joint SCC (JSCC). To the best of our knowledge, we are the first to use the sparse representation recovery residual to cluster HSIs. Based on a structured dictionary constructed by <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-means and <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-nearest neighbor (KNN), an SCC model is constructed to cluster HSIs according to the recovery residual minimization criterion. By dealing with a pixel-wise sparse recovery problem instead of the large-scale graph optimization problem of the whole image, the computational complexity and the time-and-memory cost are reduced to a large degree, which makes sense for practical applications. Then, by introducing the super-pixel neighborhood, a JSCC model is constructed to better explore the interpixel correlation of HSIs and further improve the clustering performance. The proposed algorithms were verified on three widely used HSIs. All the three experiments confirm the effectiveness of the proposed algorithms, which can be considered as competitive tools for use with large HSIs. |
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| AbstractList | Hyperspectral image (HSI) clustering is extremely challenging because of the complexity of the image structure. Recently, the subspace clustering algorithms have achieved competitive performance for HSIs. However, these methods generally are computationally complex and time-and-memory-consuming, given their reliance on large-scale adjacency matrix learning and graph segmentation, which limits their application to large HSIs and reduces their attractiveness in real applications. In this article, in view of this, two novel sparsity-based clustering algorithms are proposed for large HSIs, named sparse coding-based clustering (SCC) and joint SCC (JSCC). To the best of our knowledge, we are the first to use the sparse representation recovery residual to cluster HSIs. Based on a structured dictionary constructed by <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-means and <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-nearest neighbor (KNN), an SCC model is constructed to cluster HSIs according to the recovery residual minimization criterion. By dealing with a pixel-wise sparse recovery problem instead of the large-scale graph optimization problem of the whole image, the computational complexity and the time-and-memory cost are reduced to a large degree, which makes sense for practical applications. Then, by introducing the super-pixel neighborhood, a JSCC model is constructed to better explore the interpixel correlation of HSIs and further improve the clustering performance. The proposed algorithms were verified on three widely used HSIs. All the three experiments confirm the effectiveness of the proposed algorithms, which can be considered as competitive tools for use with large HSIs. Hyperspectral image (HSI) clustering is extremely challenging because of the complexity of the image structure. Recently, the subspace clustering algorithms have achieved competitive performance for HSIs. However, these methods generally are computationally complex and time-and-memory-consuming, given their reliance on large-scale adjacency matrix learning and graph segmentation, which limits their application to large HSIs and reduces their attractiveness in real applications. In this article, in view of this, two novel sparsity-based clustering algorithms are proposed for large HSIs, named sparse coding-based clustering (SCC) and joint SCC (JSCC). To the best of our knowledge, we are the first to use the sparse representation recovery residual to cluster HSIs. Based on a structured dictionary constructed by [Formula Omitted]-means and [Formula Omitted]-nearest neighbor (KNN), an SCC model is constructed to cluster HSIs according to the recovery residual minimization criterion. By dealing with a pixel-wise sparse recovery problem instead of the large-scale graph optimization problem of the whole image, the computational complexity and the time-and-memory cost are reduced to a large degree, which makes sense for practical applications. Then, by introducing the super-pixel neighborhood, a JSCC model is constructed to better explore the interpixel correlation of HSIs and further improve the clustering performance. The proposed algorithms were verified on three widely used HSIs. All the three experiments confirm the effectiveness of the proposed algorithms, which can be considered as competitive tools for use with large HSIs. |
| Author | Zhang, Hongyan Zhang, Liangpei Li, Pingxiang Zhai, Han |
| Author_xml | – sequence: 1 givenname: Han surname: Zhai fullname: Zhai, Han email: han@cug.edu.cn organization: School of Geography and Information Engineering, China University of Geosciences, Wuhan, China – sequence: 2 givenname: Hongyan orcidid: 0000-0002-7894-5755 surname: Zhang fullname: Zhang, Hongyan email: zhanghongyan@whu.edu.cn organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China – sequence: 3 givenname: Liangpei orcidid: 0000-0001-6890-3650 surname: Zhang fullname: Zhang, Liangpei organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China – sequence: 4 givenname: Pingxiang surname: Li fullname: Li, Pingxiang organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China |
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| SubjectTerms | Algorithms Biological system modeling Clustering Clustering algorithms Clustering methods Complexity Computational modeling Computer applications Dictionaries Encoding hyperspectral image (HSI) Hyperspectral imaging Image segmentation joint sparse coding Optimization Pixels Recovery recovery residual Remote sensing sparse coding Sparsity |
| Title | Sparsity-Based Clustering for Large Hyperspectral Remote Sensing Images |
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