Cluster-Based Tensorial Semisupervised Discriminant Analysis for Feature Extraction of SAR Images
Several features have been developed to characterize the land cover in synthetic aperture radar (SAR) data with speckle noise. Feature extraction has become an essential task for SAR image processing. However, how to preserve the original intrinsic structural information and enhance the discriminant...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8744595/ |
| _version_ | 1819276145851367424 |
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| author | Xiaoying Wu Xianbin Wen Liming Yuan Changlun Guo Haixia Xu |
| author_facet | Xiaoying Wu Xianbin Wen Liming Yuan Changlun Guo Haixia Xu |
| author_sort | Xiaoying Wu |
| collection | DOAJ |
| description | Several features have been developed to characterize the land cover in synthetic aperture radar (SAR) data with speckle noise. Feature extraction has become an essential task for SAR image processing. However, how to preserve the original intrinsic structural information and enhance the discriminant ability to reduce the impact of noise is still a challenge in this area. In this paper, using a clustering method to maintain the nonlocal information in images and tensors with the ability to preserve spatial neighborhood structure information, a new cluster-based tensorial semisupervised discriminant analysis (CTSDA) method is proposed for feature extraction of SAR images. In the CTSDA, the block clustering algorithm is employed to generate several high-order clustering tensors of multifeature SAR images, which preserves the intrinsic nonlocal spatial information and neighborhood structure. In the multiple manifold structures of the cluster tensors, the improved discriminant analysis enhances the feature discrimination by considering the local structure and labeled and unlabeled information through the Laplace matrix, and the fusion of tensor algebraic analysis and improved discriminant analysis produces multiple new projection directions of the cluster tensors. Finally, feature extraction is achieved by rearranging the projected cluster tensors. The experimental results on the simulated SAR data and four real SAR images demonstrate the superiority of the proposed method over several state-of-the-art approaches. |
| first_indexed | 2024-12-23T23:35:34Z |
| format | Article |
| id | doaj.art-83007d612ccf41a29283a9f59c6b901b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:35:34Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-83007d612ccf41a29283a9f59c6b901b2022-12-21T17:25:52ZengIEEEIEEE Access2169-35362019-01-017843188433210.1109/ACCESS.2019.29246768744595Cluster-Based Tensorial Semisupervised Discriminant Analysis for Feature Extraction of SAR ImagesXiaoying Wu0https://orcid.org/0000-0002-6974-6041Xianbin Wen1Liming Yuan2Changlun Guo3Haixia Xu4School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, ChinaSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin, ChinaSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin, ChinaSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin, ChinaSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin, ChinaSeveral features have been developed to characterize the land cover in synthetic aperture radar (SAR) data with speckle noise. Feature extraction has become an essential task for SAR image processing. However, how to preserve the original intrinsic structural information and enhance the discriminant ability to reduce the impact of noise is still a challenge in this area. In this paper, using a clustering method to maintain the nonlocal information in images and tensors with the ability to preserve spatial neighborhood structure information, a new cluster-based tensorial semisupervised discriminant analysis (CTSDA) method is proposed for feature extraction of SAR images. In the CTSDA, the block clustering algorithm is employed to generate several high-order clustering tensors of multifeature SAR images, which preserves the intrinsic nonlocal spatial information and neighborhood structure. In the multiple manifold structures of the cluster tensors, the improved discriminant analysis enhances the feature discrimination by considering the local structure and labeled and unlabeled information through the Laplace matrix, and the fusion of tensor algebraic analysis and improved discriminant analysis produces multiple new projection directions of the cluster tensors. Finally, feature extraction is achieved by rearranging the projected cluster tensors. The experimental results on the simulated SAR data and four real SAR images demonstrate the superiority of the proposed method over several state-of-the-art approaches.https://ieeexplore.ieee.org/document/8744595/Feature extractionnonlocaltensorsemisupervised discriminant analysisSAR |
| spellingShingle | Xiaoying Wu Xianbin Wen Liming Yuan Changlun Guo Haixia Xu Cluster-Based Tensorial Semisupervised Discriminant Analysis for Feature Extraction of SAR Images IEEE Access Feature extraction nonlocal tensor semisupervised discriminant analysis SAR |
| title | Cluster-Based Tensorial Semisupervised Discriminant Analysis for Feature Extraction of SAR Images |
| title_full | Cluster-Based Tensorial Semisupervised Discriminant Analysis for Feature Extraction of SAR Images |
| title_fullStr | Cluster-Based Tensorial Semisupervised Discriminant Analysis for Feature Extraction of SAR Images |
| title_full_unstemmed | Cluster-Based Tensorial Semisupervised Discriminant Analysis for Feature Extraction of SAR Images |
| title_short | Cluster-Based Tensorial Semisupervised Discriminant Analysis for Feature Extraction of SAR Images |
| title_sort | cluster based tensorial semisupervised discriminant analysis for feature extraction of sar images |
| topic | Feature extraction nonlocal tensor semisupervised discriminant analysis SAR |
| url | https://ieeexplore.ieee.org/document/8744595/ |
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