Simultaneous Diagonalization of Hermitian Matrices and its Application in PolSAR Ship Detection
A challenging issue in the field of marine remote sensing is the application of polarimetric synthetic aperture radar (PolSAR) to small ship detection in complicated environments. Several outstanding polarimetric detectors (such as the optimal polarimetric detector, polarimetric whitening filter, an...
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| Published in: | IEEE transactions on geoscience and remote sensing Vol. 61; p. 1 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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IEEE
01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0196-2892, 1558-0644 |
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| Abstract | A challenging issue in the field of marine remote sensing is the application of polarimetric synthetic aperture radar (PolSAR) to small ship detection in complicated environments. Several outstanding polarimetric detectors (such as the optimal polarimetric detector, polarimetric whitening filter, and polarimetric notch filter, etc.), have been effectively implemented in practical applications. A linear combination model based on quadratic optimization is summarized to establish a general framework for polarimetric detectors, transitioning the PolSAR ship target detection from a model driven approach to a hybrid (model/data)-driven approach. However, the dimension of the covariance matrix may be high, and the computation cost will be large. The higher dimension of the covariance matrix requires a bigger the data demand. As a result, when the sample size is small, the model performance will degrade. In this paper, to decrease the computational complexity and improve the robustness, we propose a novel method called the simultaneous diagonalization transform (SDT). The proposed method enables an almost simplest representation of information from the covariance matrix providing a rapid detection algorithm. The simulation experiments demonstrate that polarimetric detectors based on SDT consistently outperform those based on other methods in terms of accuracy, efficiency, and sample size requirements across various complex backgrounds. Furthermore, the effectiveness, robust, and fastness of the polarimetric detector based on SDT is validated using real data collected by RadarSAT-2, GaoFen-3, and Sentinel-1A. |
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| AbstractList | A challenging issue in the field of marine remote sensing is the application of polarimetric synthetic aperture radar (PolSAR) to small ship detection in complicated environments. Several outstanding polarimetric detectors (such as the optimal polarimetric detector, polarimetric whitening filter (PWF), and polarimetric notch filter (PNF)) have been effectively implemented in practical applications. A linear combination model based on quadratic optimization is summarized to establish a general framework for polarimetric detectors, transitioning the PolSAR ship target detection from a model-driven approach to a hybrid (model/data)-driven approach. However, the dimension of the covariance matrix may be high, and the computation cost will be large. The higher dimension of the covariance matrix requires a bigger data demand. As a result, when the sample size is small, the model performance will degrade. In this article, to decrease the computational complexity and improve the robustness, we propose a novel method called the simultaneous diagonalization transform (SDT). The proposed method enables an almost simplest representation of information from the covariance matrix providing a rapid detection algorithm. The simulation experiments demonstrate that polarimetric detectors based on SDT consistently outperform those based on other methods in terms of accuracy, efficiency, and sample size requirements across various complex backgrounds. Furthermore, the effectiveness, robust, and fastness of the polarimetric detector based on SDT is validated using real data collected by RadarSAT-2 (RS-2), GaoFen-3 (GF-3), and Sentinel-1A. A challenging issue in the field of marine remote sensing is the application of polarimetric synthetic aperture radar (PolSAR) to small ship detection in complicated environments. Several outstanding polarimetric detectors (such as the optimal polarimetric detector, polarimetric whitening filter, and polarimetric notch filter, etc.), have been effectively implemented in practical applications. A linear combination model based on quadratic optimization is summarized to establish a general framework for polarimetric detectors, transitioning the PolSAR ship target detection from a model driven approach to a hybrid (model/data)-driven approach. However, the dimension of the covariance matrix may be high, and the computation cost will be large. The higher dimension of the covariance matrix requires a bigger the data demand. As a result, when the sample size is small, the model performance will degrade. In this paper, to decrease the computational complexity and improve the robustness, we propose a novel method called the simultaneous diagonalization transform (SDT). The proposed method enables an almost simplest representation of information from the covariance matrix providing a rapid detection algorithm. The simulation experiments demonstrate that polarimetric detectors based on SDT consistently outperform those based on other methods in terms of accuracy, efficiency, and sample size requirements across various complex backgrounds. Furthermore, the effectiveness, robust, and fastness of the polarimetric detector based on SDT is validated using real data collected by RadarSAT-2, GaoFen-3, and Sentinel-1A. |
| Author | Marino, Armando Chen, Si-Wei Liu, Tao Yang, Ziyuan Gao, Gui |
| Author_xml | – sequence: 1 givenname: Tao orcidid: 0000-0002-9596-4536 surname: Liu fullname: Liu, Tao organization: School of electronic engineering, Naval University of Engineering, Wuhan, China – sequence: 2 givenname: Ziyuan orcidid: 0000-0001-7122-4173 surname: Yang fullname: Yang, Ziyuan organization: School of electronic engineering, Naval University of Engineering, Wuhan, China – sequence: 3 givenname: Gui surname: Gao fullname: Gao, Gui organization: Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu, China – sequence: 4 givenname: Armando orcidid: 0000-0002-4531-3102 surname: Marino fullname: Marino, Armando organization: Faculty of Natural Sciences, University of Stirling, UK – sequence: 5 givenname: Si-Wei orcidid: 0000-0001-8713-7664 surname: Chen fullname: Chen, Si-Wei organization: State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China |
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| Cites_doi | 10.21105/joss.00861 10.1109/tgrs.2021.3055801 10.1109/tgrs.2019.2921629 10.1109/lgrs.2010.2090492 10.1109/tgrs.2016.2641041 10.1109/tgrs.2009.2019269 10.1007/s11042-017-4693-y 10.5589/m04-002 10.1109/joe.2012.2198931 10.1109/icaca.2016.7887916 10.1109/tap.1987.1144209 10.1109/IGARSS.1988.570053 10.1109/tgrs.2022.3198940 10.1117/12.177180 10.1109/TGRS.2020.2978268 10.1109/jstars.2022.3211431 10.5589/m11-054 10.1109/jstars.2017.2671904 10.1109/tgrs.2022.3217336 10.1109/tgrs.2022.3148979 10.1109/7.18677 10.1109/lgrs.2020.3020052 10.1109/lgrs.2004.830127 10.1109/tgrs.2022.3222691 10.1109/tgrs.2015.2402312 10.1109/72.80230 10.1137/040608830 10.1109/tgrs.2020.3022181 10.1109/62.63157 10.1109/7.53442 10.1017/cbo9780511810817 10.1109/lgrs.2021.3090368 10.1109/tcyb.2023.3267947 10.1109/tgrs.2017.2701813 10.1109/lgrs.2021.3121100 |
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| SubjectTerms | Algorithms Clutter Complexity Computation Covariance matrices Covariance matrix Detection Detectors General Framework Gold Lasso regression Marine vehicles Notch filters Optimization Performance degradation Polarimetric synthetic aperture radar (PolSAR) Polarimetry Quadratic optimization Radarsat Remote sensing Robustness (mathematics) SAR (radar) Sensors Ship detection Simultaneous diagonalization Synthetic aperture radar Target detection |
| Title | Simultaneous Diagonalization of Hermitian Matrices and its Application in PolSAR Ship Detection |
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