Wishart distance-based joint collaborative representation for polarimetric SAR image classification
Inspired by collaborative representation classifier (CRC), a Wishart distance-based joint CRC (W-JCRC) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. Since that neighbouring pixels usually belong to the same category with high probability, they can be simultaneo...
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| Published in | IET radar, sonar & navigation Vol. 11; no. 11; pp. 1620 - 1628 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
The Institution of Engineering and Technology
01.11.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1751-8784 1751-8792 |
| DOI | 10.1049/iet-rsn.2017.0056 |
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| Abstract | Inspired by collaborative representation classifier (CRC), a Wishart distance-based joint CRC (W-JCRC) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. Since that neighbouring pixels usually belong to the same category with high probability, they can be simultaneously represented via a joint representation model of linear combinations of labelled samples. The joint collaborative representation of neighbouring pixels can overcome the influence of speckle noise at the same time. Considering the statistical property of PolSAR data, a weighted regularisation term with revised Wishart distance is designed to contain the correlations between unlabelled and labelled samples. The coefficients of representation are estimated by an $l_2$l2-norm minimisation derived closed-form solution. In the experiments, three real PolSAR images are applied to evaluate the performance, and the experimental results demonstrate that the proposed method is able to improve classification accuracies compared with other state-of-the-art methods. |
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| AbstractList | Inspired by collaborative representation classifier (CRC), a Wishart distance‐based joint CRC (W‐JCRC) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. Since that neighbouring pixels usually belong to the same category with high probability, they can be simultaneously represented via a joint representation model of linear combinations of labelled samples. The joint collaborative representation of neighbouring pixels can overcome the influence of speckle noise at the same time. Considering the statistical property of PolSAR data, a weighted regularisation term with revised Wishart distance is designed to contain the correlations between unlabelled and labelled samples. The coefficients of representation are estimated by an ‐norm minimisation derived closed‐form solution. In the experiments, three real PolSAR images are applied to evaluate the performance, and the experimental results demonstrate that the proposed method is able to improve classification accuracies compared with other state‐of‐the‐art methods. Inspired by collaborative representation classifier (CRC), a Wishart distance-based joint CRC (W-JCRC) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. Since that neighbouring pixels usually belong to the same category with high probability, they can be simultaneously represented via a joint representation model of linear combinations of labelled samples. The joint collaborative representation of neighbouring pixels can overcome the influence of speckle noise at the same time. Considering the statistical property of PolSAR data, a weighted regularisation term with revised Wishart distance is designed to contain the correlations between unlabelled and labelled samples. The coefficients of representation are estimated by an $l_2$l2-norm minimisation derived closed-form solution. In the experiments, three real PolSAR images are applied to evaluate the performance, and the experimental results demonstrate that the proposed method is able to improve classification accuracies compared with other state-of-the-art methods. Inspired by collaborative representation classifier (CRC), a Wishart distance‐based joint CRC (W‐JCRC) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. Since that neighbouring pixels usually belong to the same category with high probability, they can be simultaneously represented via a joint representation model of linear combinations of labelled samples. The joint collaborative representation of neighbouring pixels can overcome the influence of speckle noise at the same time. Considering the statistical property of PolSAR data, a weighted regularisation term with revised Wishart distance is designed to contain the correlations between unlabelled and labelled samples. The coefficients of representation are estimated by an l2‐norm minimisation derived closed‐form solution. In the experiments, three real PolSAR images are applied to evaluate the performance, and the experimental results demonstrate that the proposed method is able to improve classification accuracies compared with other state‐of‐the‐art methods. |
| Author | Wang, Bing Fan, Jianchao Wang, Hongyu Ma, Xiaorui Geng, Jie |
| Author_xml | – sequence: 1 givenname: Jie orcidid: 0000-0003-4858-823X surname: Geng fullname: Geng, Jie organization: 1School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China – sequence: 2 givenname: Hongyu surname: Wang fullname: Wang, Hongyu email: whyu@dlut.edu.cn organization: 1School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China – sequence: 3 givenname: Jianchao surname: Fan fullname: Fan, Jianchao organization: 2Department of Ocean Remote Sensing, National Marine Environmental Monitoring Center, Dalian 116023, People's Republic of China – sequence: 4 givenname: Xiaorui surname: Ma fullname: Ma, Xiaorui organization: 1School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China – sequence: 5 givenname: Bing surname: Wang fullname: Wang, Bing organization: 3State Grid JILIN Maintenance Company, Changchun 130000, People's Republic of China |
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| Keywords | synthetic aperture radar polarimetric synthetic aperture radar collaborative representation classifier image classification PolSAR Wishart distance-based joint collaborative representation radar imaging statistical analysis radar polarimetry polarimetric SAR image classification |
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| Snippet | Inspired by collaborative representation classifier (CRC), a Wishart distance-based joint CRC (W-JCRC) is proposed for polarimetric synthetic aperture radar... Inspired by collaborative representation classifier (CRC), a Wishart distance‐based joint CRC (W‐JCRC) is proposed for polarimetric synthetic aperture radar... |
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| SubjectTerms | collaborative representation classifier image classification polarimetric SAR image classification polarimetric synthetic aperture radar PolSAR radar imaging radar polarimetry Research Article statistical analysis synthetic aperture radar Wishart distance‐based joint collaborative representation |
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| Title | Wishart distance-based joint collaborative representation for polarimetric SAR image classification |
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