Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization
To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the character...
Ausführliche Beschreibung
Autor*in: |
Lei Yu [verfasserIn] Chunsheng Li [verfasserIn] Jie Chen [verfasserIn] Pengbo Wang [verfasserIn] Zhirong Men [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Schlagwörter: |
synthetic aperture radar (SAR) multicomponent polynomial phase signal(mc-PPS) |
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Übergeordnetes Werk: |
In: Remote Sensing - MDPI AG, 2009, 12(2020), 20, p 3302 |
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Übergeordnetes Werk: |
volume:12 ; year:2020 ; number:20, p 3302 |
Links: |
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DOI / URN: |
10.3390/rs12203302 |
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Katalog-ID: |
DOAJ004710436 |
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10.3390/rs12203302 doi (DE-627)DOAJ004710436 (DE-599)DOAJd8202ef549c94b0aac1122de7db3d9ac DE-627 ger DE-627 rakwb eng Lei Yu verfasserin aut Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO. synthetic aperture radar (SAR) moving vessel multicomponent polynomial phase signal(mc-PPS) adaptive joint time-frequency (AJTF) decomposition co-evolutionary particle swarm optimization Science Q Chunsheng Li verfasserin aut Jie Chen verfasserin aut Pengbo Wang verfasserin aut Zhirong Men verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 20, p 3302 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:20, p 3302 https://doi.org/10.3390/rs12203302 kostenfrei https://doaj.org/article/d8202ef549c94b0aac1122de7db3d9ac kostenfrei https://www.mdpi.com/2072-4292/12/20/3302 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 20, p 3302 |
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10.3390/rs12203302 doi (DE-627)DOAJ004710436 (DE-599)DOAJd8202ef549c94b0aac1122de7db3d9ac DE-627 ger DE-627 rakwb eng Lei Yu verfasserin aut Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO. synthetic aperture radar (SAR) moving vessel multicomponent polynomial phase signal(mc-PPS) adaptive joint time-frequency (AJTF) decomposition co-evolutionary particle swarm optimization Science Q Chunsheng Li verfasserin aut Jie Chen verfasserin aut Pengbo Wang verfasserin aut Zhirong Men verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 20, p 3302 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:20, p 3302 https://doi.org/10.3390/rs12203302 kostenfrei https://doaj.org/article/d8202ef549c94b0aac1122de7db3d9ac kostenfrei https://www.mdpi.com/2072-4292/12/20/3302 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 20, p 3302 |
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10.3390/rs12203302 doi (DE-627)DOAJ004710436 (DE-599)DOAJd8202ef549c94b0aac1122de7db3d9ac DE-627 ger DE-627 rakwb eng Lei Yu verfasserin aut Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO. synthetic aperture radar (SAR) moving vessel multicomponent polynomial phase signal(mc-PPS) adaptive joint time-frequency (AJTF) decomposition co-evolutionary particle swarm optimization Science Q Chunsheng Li verfasserin aut Jie Chen verfasserin aut Pengbo Wang verfasserin aut Zhirong Men verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 20, p 3302 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:20, p 3302 https://doi.org/10.3390/rs12203302 kostenfrei https://doaj.org/article/d8202ef549c94b0aac1122de7db3d9ac kostenfrei https://www.mdpi.com/2072-4292/12/20/3302 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 20, p 3302 |
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10.3390/rs12203302 doi (DE-627)DOAJ004710436 (DE-599)DOAJd8202ef549c94b0aac1122de7db3d9ac DE-627 ger DE-627 rakwb eng Lei Yu verfasserin aut Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO. synthetic aperture radar (SAR) moving vessel multicomponent polynomial phase signal(mc-PPS) adaptive joint time-frequency (AJTF) decomposition co-evolutionary particle swarm optimization Science Q Chunsheng Li verfasserin aut Jie Chen verfasserin aut Pengbo Wang verfasserin aut Zhirong Men verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 20, p 3302 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:20, p 3302 https://doi.org/10.3390/rs12203302 kostenfrei https://doaj.org/article/d8202ef549c94b0aac1122de7db3d9ac kostenfrei https://www.mdpi.com/2072-4292/12/20/3302 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 20, p 3302 |
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10.3390/rs12203302 doi (DE-627)DOAJ004710436 (DE-599)DOAJd8202ef549c94b0aac1122de7db3d9ac DE-627 ger DE-627 rakwb eng Lei Yu verfasserin aut Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO. synthetic aperture radar (SAR) moving vessel multicomponent polynomial phase signal(mc-PPS) adaptive joint time-frequency (AJTF) decomposition co-evolutionary particle swarm optimization Science Q Chunsheng Li verfasserin aut Jie Chen verfasserin aut Pengbo Wang verfasserin aut Zhirong Men verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 20, p 3302 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:20, p 3302 https://doi.org/10.3390/rs12203302 kostenfrei https://doaj.org/article/d8202ef549c94b0aac1122de7db3d9ac kostenfrei https://www.mdpi.com/2072-4292/12/20/3302 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 20, p 3302 |
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Lei Yu misc synthetic aperture radar (SAR) misc moving vessel misc multicomponent polynomial phase signal(mc-PPS) misc adaptive joint time-frequency (AJTF) decomposition misc co-evolutionary particle swarm optimization misc Science misc Q Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization |
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Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization synthetic aperture radar (SAR) moving vessel multicomponent polynomial phase signal(mc-PPS) adaptive joint time-frequency (AJTF) decomposition co-evolutionary particle swarm optimization |
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Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization |
abstract |
To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO. |
abstractGer |
To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO. |
abstract_unstemmed |
To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO. |
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Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization |
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