Land cover classification of high-resolution remote sensing images based on improved spectral clustering.
Applying unsupervised classification techniques on remote sensing images enables rapid land cover classification. Using remote sensing imagery from the ZY1-02D satellite's VNIC and AHSI cameras as the basis, multi-source feature information encompassing spectral, edge shape, and texture feature...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316830 |
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| author | Song Wu Jian-Min Cao Xin-Yu Zhao |
| author_facet | Song Wu Jian-Min Cao Xin-Yu Zhao |
| author_sort | Song Wu |
| collection | DOAJ |
| description | Applying unsupervised classification techniques on remote sensing images enables rapid land cover classification. Using remote sensing imagery from the ZY1-02D satellite's VNIC and AHSI cameras as the basis, multi-source feature information encompassing spectral, edge shape, and texture features was extracted as the data source. The Lanczos algorithm, which determines the largest eigenpairs of a high-order matrix, was integrated with the spectral clustering algorithm to solve for eigenvalues and eigenvectors. The results indicate that this method can quickly and effectively classify land cover. The classification accuracy was significantly improved by incorporating multi-source feature information, with a kappa coefficient reaching 0.846. Compared to traditional classification methods, the improved spectral clustering algorithm demonstrated better adaptability to data distribution and superior clustering performance. This suggests that the method has strong recognition capabilities for pixels with complex spatial shapes, making it a high-performance, unsupervised classification approach. |
| format | Article |
| id | doaj-art-bbc3192ac2c1417b85c06c0b9b6883fd |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-bbc3192ac2c1417b85c06c0b9b6883fd2025-02-12T05:30:50ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031683010.1371/journal.pone.0316830Land cover classification of high-resolution remote sensing images based on improved spectral clustering.Song WuJian-Min CaoXin-Yu ZhaoApplying unsupervised classification techniques on remote sensing images enables rapid land cover classification. Using remote sensing imagery from the ZY1-02D satellite's VNIC and AHSI cameras as the basis, multi-source feature information encompassing spectral, edge shape, and texture features was extracted as the data source. The Lanczos algorithm, which determines the largest eigenpairs of a high-order matrix, was integrated with the spectral clustering algorithm to solve for eigenvalues and eigenvectors. The results indicate that this method can quickly and effectively classify land cover. The classification accuracy was significantly improved by incorporating multi-source feature information, with a kappa coefficient reaching 0.846. Compared to traditional classification methods, the improved spectral clustering algorithm demonstrated better adaptability to data distribution and superior clustering performance. This suggests that the method has strong recognition capabilities for pixels with complex spatial shapes, making it a high-performance, unsupervised classification approach.https://doi.org/10.1371/journal.pone.0316830 |
| spellingShingle | Song Wu Jian-Min Cao Xin-Yu Zhao Land cover classification of high-resolution remote sensing images based on improved spectral clustering. PLoS ONE |
| title | Land cover classification of high-resolution remote sensing images based on improved spectral clustering. |
| title_full | Land cover classification of high-resolution remote sensing images based on improved spectral clustering. |
| title_fullStr | Land cover classification of high-resolution remote sensing images based on improved spectral clustering. |
| title_full_unstemmed | Land cover classification of high-resolution remote sensing images based on improved spectral clustering. |
| title_short | Land cover classification of high-resolution remote sensing images based on improved spectral clustering. |
| title_sort | land cover classification of high resolution remote sensing images based on improved spectral clustering |
| url | https://doi.org/10.1371/journal.pone.0316830 |
| work_keys_str_mv | AT songwu landcoverclassificationofhighresolutionremotesensingimagesbasedonimprovedspectralclustering AT jianmincao landcoverclassificationofhighresolutionremotesensingimagesbasedonimprovedspectralclustering AT xinyuzhao landcoverclassificationofhighresolutionremotesensingimagesbasedonimprovedspectralclustering |