Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection

Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-...
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Autor*in:	Wei Xue [verfasserIn] Jiahao Qi [verfasserIn] Guoqing Shao [verfasserIn] Zixuan Xiao [verfasserIn] Yu Zhang [verfasserIn] Ping Zhong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Fixed-wing unmanned aerial vehicle (UAV) infrared dim small target detection low-rank matrix approximation nuclear norm relaxation sparse constraint

Übergeordnetes Werk:	In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 14(2021), Seite 4150-4166
Übergeordnetes Werk:	volume:14 ; year:2021 ; pages:4150-4166

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JSTARS.2021.3069032

Katalog-ID:	DOAJ062823728

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650		4	\|a Fixed-wing unmanned aerial vehicle (UAV)
650		4	\|a infrared dim small target detection
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700	0		\|a Guoqing Shao \|e verfasserin \|4 aut
700	0		\|a Zixuan Xiao \|e verfasserin \|4 aut
700	0		\|a Yu Zhang \|e verfasserin \|4 aut
700	0		\|a Ping Zhong \|e verfasserin \|4 aut
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allfields	10.1109/JSTARS.2021.3069032 doi (DE-627)DOAJ062823728 (DE-599)DOAJaccd453d54cd4164906e0ec767a88eb3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Wei Xue verfasserin aut Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula<-norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula< matrix norm and the <inline-formula<<tex-math notation="LaTeX"<$l_{2,1}$</tex-math<</inline-formula< matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis. Fixed-wing unmanned aerial vehicle (UAV) infrared dim small target detection low-rank matrix approximation nuclear norm relaxation sparse constraint Ocean engineering Geophysics. Cosmic physics Jiahao Qi verfasserin aut Guoqing Shao verfasserin aut Zixuan Xiao verfasserin aut Yu Zhang verfasserin aut Ping Zhong verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4150-4166 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4150-4166 https://doi.org/10.1109/JSTARS.2021.3069032 kostenfrei https://doaj.org/article/accd453d54cd4164906e0ec767a88eb3 kostenfrei https://ieeexplore.ieee.org/document/9387528/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 4150-4166
spelling	10.1109/JSTARS.2021.3069032 doi (DE-627)DOAJ062823728 (DE-599)DOAJaccd453d54cd4164906e0ec767a88eb3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Wei Xue verfasserin aut Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula<-norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula< matrix norm and the <inline-formula<<tex-math notation="LaTeX"<$l_{2,1}$</tex-math<</inline-formula< matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis. Fixed-wing unmanned aerial vehicle (UAV) infrared dim small target detection low-rank matrix approximation nuclear norm relaxation sparse constraint Ocean engineering Geophysics. Cosmic physics Jiahao Qi verfasserin aut Guoqing Shao verfasserin aut Zixuan Xiao verfasserin aut Yu Zhang verfasserin aut Ping Zhong verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4150-4166 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4150-4166 https://doi.org/10.1109/JSTARS.2021.3069032 kostenfrei https://doaj.org/article/accd453d54cd4164906e0ec767a88eb3 kostenfrei https://ieeexplore.ieee.org/document/9387528/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 4150-4166
allfields_unstemmed	10.1109/JSTARS.2021.3069032 doi (DE-627)DOAJ062823728 (DE-599)DOAJaccd453d54cd4164906e0ec767a88eb3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Wei Xue verfasserin aut Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula<-norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula< matrix norm and the <inline-formula<<tex-math notation="LaTeX"<$l_{2,1}$</tex-math<</inline-formula< matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis. Fixed-wing unmanned aerial vehicle (UAV) infrared dim small target detection low-rank matrix approximation nuclear norm relaxation sparse constraint Ocean engineering Geophysics. Cosmic physics Jiahao Qi verfasserin aut Guoqing Shao verfasserin aut Zixuan Xiao verfasserin aut Yu Zhang verfasserin aut Ping Zhong verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4150-4166 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4150-4166 https://doi.org/10.1109/JSTARS.2021.3069032 kostenfrei https://doaj.org/article/accd453d54cd4164906e0ec767a88eb3 kostenfrei https://ieeexplore.ieee.org/document/9387528/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 4150-4166
allfieldsGer	10.1109/JSTARS.2021.3069032 doi (DE-627)DOAJ062823728 (DE-599)DOAJaccd453d54cd4164906e0ec767a88eb3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Wei Xue verfasserin aut Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula<-norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula< matrix norm and the <inline-formula<<tex-math notation="LaTeX"<$l_{2,1}$</tex-math<</inline-formula< matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis. Fixed-wing unmanned aerial vehicle (UAV) infrared dim small target detection low-rank matrix approximation nuclear norm relaxation sparse constraint Ocean engineering Geophysics. Cosmic physics Jiahao Qi verfasserin aut Guoqing Shao verfasserin aut Zixuan Xiao verfasserin aut Yu Zhang verfasserin aut Ping Zhong verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4150-4166 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4150-4166 https://doi.org/10.1109/JSTARS.2021.3069032 kostenfrei https://doaj.org/article/accd453d54cd4164906e0ec767a88eb3 kostenfrei https://ieeexplore.ieee.org/document/9387528/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 4150-4166
allfieldsSound	10.1109/JSTARS.2021.3069032 doi (DE-627)DOAJ062823728 (DE-599)DOAJaccd453d54cd4164906e0ec767a88eb3 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Wei Xue verfasserin aut Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula<-norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula< matrix norm and the <inline-formula<<tex-math notation="LaTeX"<$l_{2,1}$</tex-math<</inline-formula< matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis. Fixed-wing unmanned aerial vehicle (UAV) infrared dim small target detection low-rank matrix approximation nuclear norm relaxation sparse constraint Ocean engineering Geophysics. Cosmic physics Jiahao Qi verfasserin aut Guoqing Shao verfasserin aut Zixuan Xiao verfasserin aut Yu Zhang verfasserin aut Ping Zhong verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4150-4166 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4150-4166 https://doi.org/10.1109/JSTARS.2021.3069032 kostenfrei https://doaj.org/article/accd453d54cd4164906e0ec767a88eb3 kostenfrei https://ieeexplore.ieee.org/document/9387528/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 4150-4166
language	English
source	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14(2021), Seite 4150-4166 volume:14 year:2021 pages:4150-4166
sourceStr	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14(2021), Seite 4150-4166 volume:14 year:2021 pages:4150-4166
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Fixed-wing unmanned aerial vehicle (UAV) infrared dim small target detection low-rank matrix approximation nuclear norm relaxation sparse constraint Ocean engineering Geophysics. Cosmic physics
isfreeaccess_bool	true
container_title	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
authorswithroles_txt_mv	Wei Xue @@aut@@ Jiahao Qi @@aut@@ Guoqing Shao @@aut@@ Zixuan Xiao @@aut@@ Yu Zhang @@aut@@ Ping Zhong @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	581732634
id	DOAJ062823728
language_de	englisch
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callnumber-first	T - Technology
author	Wei Xue
spellingShingle	Wei Xue misc TC1501-1800 misc QC801-809 misc Fixed-wing unmanned aerial vehicle (UAV) misc infrared dim small target detection misc low-rank matrix approximation misc nuclear norm relaxation misc sparse constraint misc Ocean engineering misc Geophysics. Cosmic physics Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection
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topic_title	TC1501-1800 QC801-809 Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection Fixed-wing unmanned aerial vehicle (UAV) infrared dim small target detection low-rank matrix approximation nuclear norm relaxation sparse constraint
topic	misc TC1501-1800 misc QC801-809 misc Fixed-wing unmanned aerial vehicle (UAV) misc infrared dim small target detection misc low-rank matrix approximation misc nuclear norm relaxation misc sparse constraint misc Ocean engineering misc Geophysics. Cosmic physics
topic_unstemmed	misc TC1501-1800 misc QC801-809 misc Fixed-wing unmanned aerial vehicle (UAV) misc infrared dim small target detection misc low-rank matrix approximation misc nuclear norm relaxation misc sparse constraint misc Ocean engineering misc Geophysics. Cosmic physics
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title	Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection
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title_full	Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection
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title_sort	low-rank approximation and multiple sparse constraint modeling for infrared low-flying fixed-wing uav detection
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title_auth	Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection
abstract	Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula<-norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula< matrix norm and the <inline-formula<<tex-math notation="LaTeX"<$l_{2,1}$</tex-math<</inline-formula< matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis.
abstractGer	Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula<-norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula< matrix norm and the <inline-formula<<tex-math notation="LaTeX"<$l_{2,1}$</tex-math<</inline-formula< matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis.
abstract_unstemmed	Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula<-norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula< matrix norm and the <inline-formula<<tex-math notation="LaTeX"<$l_{2,1}$</tex-math<</inline-formula< matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis.
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title_short	Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection
url	https://doi.org/10.1109/JSTARS.2021.3069032 https://doaj.org/article/accd453d54cd4164906e0ec767a88eb3 https://ieeexplore.ieee.org/document/9387528/ https://doaj.org/toc/2151-1535
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up_date	2024-07-03T14:15:11.972Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ062823728</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309023252.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/JSTARS.2021.3069032</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ062823728</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJaccd453d54cd4164906e0ec767a88eb3</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TC1501-1800</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC801-809</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Wei Xue</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula<-norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the <inline-formula<<tex-math notation="LaTeX"<$l_{1,1}$</tex-math<</inline-formula< matrix norm and the <inline-formula<<tex-math notation="LaTeX"<$l_{2,1}$</tex-math<</inline-formula< matrix norm are used to constrain the noise patch. 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Cosmic physics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jiahao Qi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Guoqing Shao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zixuan Xiao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yu Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ping Zhong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Journal of Selected Topics in Applied Earth 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Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection

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