HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION
In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/945/2016/isprs-archives-XLI-B3-945-2016.pdf |
| _version_ | 1818701181226057728 |
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| author | H. Zhang H. Zhang H. Zhai W. Liao L. Cao L. Zhang A. Pižurica |
| author_facet | H. Zhang H. Zhang H. Zhai W. Liao L. Cao L. Zhang A. Pižurica |
| author_sort | H. Zhang |
| collection | DOAJ |
| description | In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for
hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space
with a kernel strategy. In particular, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better
explore the complex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient
matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother
clustering result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and
outperforms the state-of-the-art clustering methods. |
| first_indexed | 2024-12-17T15:16:45Z |
| format | Article |
| id | doaj.art-1d99df22fb804b95963ed93d13f55b4c |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-17T15:16:45Z |
| publishDate | 2016-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-1d99df22fb804b95963ed93d13f55b4c2022-12-21T21:43:30ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342016-06-01XLI-B394594810.5194/isprs-archives-XLI-B3-945-2016HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATIONH. Zhang0H. Zhang1H. Zhai2W. Liao3L. Cao4L. Zhang5A. Pižurica6Ghent University, Dept. Telecommunications and Information Processing, TELIN-IPI-iMinds, Ghent, BelgiumThe State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, ChinaThe State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, ChinaGhent University, Dept. Telecommunications and Information Processing, TELIN-IPI-iMinds, Ghent, BelgiumSchool of Printing and Packaging, Wuhan University, ChinaThe State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, ChinaGhent University, Dept. Telecommunications and Information Processing, TELIN-IPI-iMinds, Ghent, BelgiumIn this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better explore the complex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother clustering result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and outperforms the state-of-the-art clustering methods.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/945/2016/isprs-archives-XLI-B3-945-2016.pdf |
| spellingShingle | H. Zhang H. Zhang H. Zhai W. Liao L. Cao L. Zhang A. Pižurica HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION |
| title_full | HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION |
| title_fullStr | HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION |
| title_full_unstemmed | HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION |
| title_short | HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION |
| title_sort | hyperspectral image kernel sparse subspace clustering with spatial max pooling operation |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/945/2016/isprs-archives-XLI-B3-945-2016.pdf |
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