Unsupervised SAR image segmentation based on kernel TMFs with belief propagation

The triplet Markov field (TMF) model has achieved promising results in synthetic aperture radar (SAR) image segmentation. Focusing on the simple likelihood modelling of an SAR image and the effective optimisation of the TMF model, an unsupervised SAR image segmentation algorithm based on kernel TMF...
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Gespeichert in:

Autor*in:	Lu Gan [verfasserIn] Xiaoming Liu [verfasserIn] Ziwei Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	synthetic aperture radar speckle maximum likelihood estimation image segmentation markov processes radar imaging simulated sar images real sar images triplet markov field model synthetic aperture radar image segmentation simple likelihood modelling effective optimisation unsupervised sar image segmentation algorithm complex speckle noise statistical models piecewise constant likelihood model kernel mapped space sar image data higher dimension space kernel function max-product belief propagation algorithm kernel tmf model

Übergeordnetes Werk:	In: The Journal of Engineering - Wiley, 2013, (2019)
Übergeordnetes Werk:	year:2019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1049/joe.2019.0428

Katalog-ID:	DOAJ047421835

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520			\|a The triplet Markov field (TMF) model has achieved promising results in synthetic aperture radar (SAR) image segmentation. Focusing on the simple likelihood modelling of an SAR image and the effective optimisation of the TMF model, an unsupervised SAR image segmentation algorithm based on kernel TMF with belief propagation is proposed in this study. Instead of complex speckle noise statistical models in a spatial domain, a piecewise constant likelihood model in the kernel mapped space is used in the TMF model by mapping the SAR image data into a higher dimension space via a kernel function. The max-product belief propagation algorithm is used to realise effective optimisation of the proposed kernel TMF model. Experiments on both simulated and real SAR images demonstrate the effectiveness of the proposed algorithm.
650		4	\|a synthetic aperture radar
650		4	\|a speckle
650		4	\|a maximum likelihood estimation
650		4	\|a image segmentation
650		4	\|a markov processes
650		4	\|a radar imaging
650		4	\|a simulated sar images
650		4	\|a real sar images
650		4	\|a triplet markov field model
650		4	\|a synthetic aperture radar image segmentation
650		4	\|a simple likelihood modelling
650		4	\|a effective optimisation
650		4	\|a unsupervised sar image segmentation algorithm
650		4	\|a complex speckle noise statistical models
650		4	\|a piecewise constant likelihood model
650		4	\|a kernel mapped space
650		4	\|a sar image data
650		4	\|a higher dimension space
650		4	\|a kernel function
650		4	\|a max-product belief propagation algorithm
650		4	\|a kernel tmf model
653		0	\|a Engineering (General). Civil engineering (General)
700	0		\|a Xiaoming Liu \|e verfasserin \|4 aut
700	0		\|a Ziwei Li \|e verfasserin \|4 aut
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allfields	10.1049/joe.2019.0428 doi (DE-627)DOAJ047421835 (DE-599)DOAJ5a97b00fda83408e823520cbff6a31bc DE-627 ger DE-627 rakwb eng TA1-2040 Lu Gan verfasserin aut Unsupervised SAR image segmentation based on kernel TMFs with belief propagation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The triplet Markov field (TMF) model has achieved promising results in synthetic aperture radar (SAR) image segmentation. Focusing on the simple likelihood modelling of an SAR image and the effective optimisation of the TMF model, an unsupervised SAR image segmentation algorithm based on kernel TMF with belief propagation is proposed in this study. Instead of complex speckle noise statistical models in a spatial domain, a piecewise constant likelihood model in the kernel mapped space is used in the TMF model by mapping the SAR image data into a higher dimension space via a kernel function. The max-product belief propagation algorithm is used to realise effective optimisation of the proposed kernel TMF model. Experiments on both simulated and real SAR images demonstrate the effectiveness of the proposed algorithm. synthetic aperture radar speckle maximum likelihood estimation image segmentation markov processes radar imaging simulated sar images real sar images triplet markov field model synthetic aperture radar image segmentation simple likelihood modelling effective optimisation unsupervised sar image segmentation algorithm complex speckle noise statistical models piecewise constant likelihood model kernel mapped space sar image data higher dimension space kernel function max-product belief propagation algorithm kernel tmf model Engineering (General). Civil engineering (General) Xiaoming Liu verfasserin aut Ziwei Li verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2019.0428 kostenfrei https://doaj.org/article/5a97b00fda83408e823520cbff6a31bc kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0428 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
spelling	10.1049/joe.2019.0428 doi (DE-627)DOAJ047421835 (DE-599)DOAJ5a97b00fda83408e823520cbff6a31bc DE-627 ger DE-627 rakwb eng TA1-2040 Lu Gan verfasserin aut Unsupervised SAR image segmentation based on kernel TMFs with belief propagation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The triplet Markov field (TMF) model has achieved promising results in synthetic aperture radar (SAR) image segmentation. Focusing on the simple likelihood modelling of an SAR image and the effective optimisation of the TMF model, an unsupervised SAR image segmentation algorithm based on kernel TMF with belief propagation is proposed in this study. Instead of complex speckle noise statistical models in a spatial domain, a piecewise constant likelihood model in the kernel mapped space is used in the TMF model by mapping the SAR image data into a higher dimension space via a kernel function. The max-product belief propagation algorithm is used to realise effective optimisation of the proposed kernel TMF model. Experiments on both simulated and real SAR images demonstrate the effectiveness of the proposed algorithm. synthetic aperture radar speckle maximum likelihood estimation image segmentation markov processes radar imaging simulated sar images real sar images triplet markov field model synthetic aperture radar image segmentation simple likelihood modelling effective optimisation unsupervised sar image segmentation algorithm complex speckle noise statistical models piecewise constant likelihood model kernel mapped space sar image data higher dimension space kernel function max-product belief propagation algorithm kernel tmf model Engineering (General). Civil engineering (General) Xiaoming Liu verfasserin aut Ziwei Li verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2019.0428 kostenfrei https://doaj.org/article/5a97b00fda83408e823520cbff6a31bc kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0428 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfields_unstemmed	10.1049/joe.2019.0428 doi (DE-627)DOAJ047421835 (DE-599)DOAJ5a97b00fda83408e823520cbff6a31bc DE-627 ger DE-627 rakwb eng TA1-2040 Lu Gan verfasserin aut Unsupervised SAR image segmentation based on kernel TMFs with belief propagation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The triplet Markov field (TMF) model has achieved promising results in synthetic aperture radar (SAR) image segmentation. Focusing on the simple likelihood modelling of an SAR image and the effective optimisation of the TMF model, an unsupervised SAR image segmentation algorithm based on kernel TMF with belief propagation is proposed in this study. Instead of complex speckle noise statistical models in a spatial domain, a piecewise constant likelihood model in the kernel mapped space is used in the TMF model by mapping the SAR image data into a higher dimension space via a kernel function. The max-product belief propagation algorithm is used to realise effective optimisation of the proposed kernel TMF model. Experiments on both simulated and real SAR images demonstrate the effectiveness of the proposed algorithm. synthetic aperture radar speckle maximum likelihood estimation image segmentation markov processes radar imaging simulated sar images real sar images triplet markov field model synthetic aperture radar image segmentation simple likelihood modelling effective optimisation unsupervised sar image segmentation algorithm complex speckle noise statistical models piecewise constant likelihood model kernel mapped space sar image data higher dimension space kernel function max-product belief propagation algorithm kernel tmf model Engineering (General). Civil engineering (General) Xiaoming Liu verfasserin aut Ziwei Li verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2019.0428 kostenfrei https://doaj.org/article/5a97b00fda83408e823520cbff6a31bc kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0428 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfieldsGer	10.1049/joe.2019.0428 doi (DE-627)DOAJ047421835 (DE-599)DOAJ5a97b00fda83408e823520cbff6a31bc DE-627 ger DE-627 rakwb eng TA1-2040 Lu Gan verfasserin aut Unsupervised SAR image segmentation based on kernel TMFs with belief propagation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The triplet Markov field (TMF) model has achieved promising results in synthetic aperture radar (SAR) image segmentation. Focusing on the simple likelihood modelling of an SAR image and the effective optimisation of the TMF model, an unsupervised SAR image segmentation algorithm based on kernel TMF with belief propagation is proposed in this study. Instead of complex speckle noise statistical models in a spatial domain, a piecewise constant likelihood model in the kernel mapped space is used in the TMF model by mapping the SAR image data into a higher dimension space via a kernel function. The max-product belief propagation algorithm is used to realise effective optimisation of the proposed kernel TMF model. Experiments on both simulated and real SAR images demonstrate the effectiveness of the proposed algorithm. synthetic aperture radar speckle maximum likelihood estimation image segmentation markov processes radar imaging simulated sar images real sar images triplet markov field model synthetic aperture radar image segmentation simple likelihood modelling effective optimisation unsupervised sar image segmentation algorithm complex speckle noise statistical models piecewise constant likelihood model kernel mapped space sar image data higher dimension space kernel function max-product belief propagation algorithm kernel tmf model Engineering (General). Civil engineering (General) Xiaoming Liu verfasserin aut Ziwei Li verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2019.0428 kostenfrei https://doaj.org/article/5a97b00fda83408e823520cbff6a31bc kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0428 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfieldsSound	10.1049/joe.2019.0428 doi (DE-627)DOAJ047421835 (DE-599)DOAJ5a97b00fda83408e823520cbff6a31bc DE-627 ger DE-627 rakwb eng TA1-2040 Lu Gan verfasserin aut Unsupervised SAR image segmentation based on kernel TMFs with belief propagation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The triplet Markov field (TMF) model has achieved promising results in synthetic aperture radar (SAR) image segmentation. Focusing on the simple likelihood modelling of an SAR image and the effective optimisation of the TMF model, an unsupervised SAR image segmentation algorithm based on kernel TMF with belief propagation is proposed in this study. Instead of complex speckle noise statistical models in a spatial domain, a piecewise constant likelihood model in the kernel mapped space is used in the TMF model by mapping the SAR image data into a higher dimension space via a kernel function. The max-product belief propagation algorithm is used to realise effective optimisation of the proposed kernel TMF model. Experiments on both simulated and real SAR images demonstrate the effectiveness of the proposed algorithm. synthetic aperture radar speckle maximum likelihood estimation image segmentation markov processes radar imaging simulated sar images real sar images triplet markov field model synthetic aperture radar image segmentation simple likelihood modelling effective optimisation unsupervised sar image segmentation algorithm complex speckle noise statistical models piecewise constant likelihood model kernel mapped space sar image data higher dimension space kernel function max-product belief propagation algorithm kernel tmf model Engineering (General). Civil engineering (General) Xiaoming Liu verfasserin aut Ziwei Li verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2019.0428 kostenfrei https://doaj.org/article/5a97b00fda83408e823520cbff6a31bc kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0428 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
language	English
source	In The Journal of Engineering (2019) year:2019
sourceStr	In The Journal of Engineering (2019) year:2019
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	synthetic aperture radar speckle maximum likelihood estimation image segmentation markov processes radar imaging simulated sar images real sar images triplet markov field model synthetic aperture radar image segmentation simple likelihood modelling effective optimisation unsupervised sar image segmentation algorithm complex speckle noise statistical models piecewise constant likelihood model kernel mapped space sar image data higher dimension space kernel function max-product belief propagation algorithm kernel tmf model Engineering (General). Civil engineering (General)
isfreeaccess_bool	true
container_title	The Journal of Engineering
authorswithroles_txt_mv	Lu Gan @@aut@@ Xiaoming Liu @@aut@@ Ziwei Li @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	75682270X
id	DOAJ047421835
language_de	englisch
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callnumber-first	T - Technology
author	Lu Gan
spellingShingle	Lu Gan misc TA1-2040 misc synthetic aperture radar misc speckle misc maximum likelihood estimation misc image segmentation misc markov processes misc radar imaging misc simulated sar images misc real sar images misc triplet markov field model misc synthetic aperture radar image segmentation misc simple likelihood modelling misc effective optimisation misc unsupervised sar image segmentation algorithm misc complex speckle noise statistical models misc piecewise constant likelihood model misc kernel mapped space misc sar image data misc higher dimension space misc kernel function misc max-product belief propagation algorithm misc kernel tmf model misc Engineering (General). Civil engineering (General) Unsupervised SAR image segmentation based on kernel TMFs with belief propagation
authorStr	Lu Gan
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topic_title	TA1-2040 Unsupervised SAR image segmentation based on kernel TMFs with belief propagation synthetic aperture radar speckle maximum likelihood estimation image segmentation markov processes radar imaging simulated sar images real sar images triplet markov field model synthetic aperture radar image segmentation simple likelihood modelling effective optimisation unsupervised sar image segmentation algorithm complex speckle noise statistical models piecewise constant likelihood model kernel mapped space sar image data higher dimension space kernel function max-product belief propagation algorithm kernel tmf model
topic	misc TA1-2040 misc synthetic aperture radar misc speckle misc maximum likelihood estimation misc image segmentation misc markov processes misc radar imaging misc simulated sar images misc real sar images misc triplet markov field model misc synthetic aperture radar image segmentation misc simple likelihood modelling misc effective optimisation misc unsupervised sar image segmentation algorithm misc complex speckle noise statistical models misc piecewise constant likelihood model misc kernel mapped space misc sar image data misc higher dimension space misc kernel function misc max-product belief propagation algorithm misc kernel tmf model misc Engineering (General). Civil engineering (General)
topic_unstemmed	misc TA1-2040 misc synthetic aperture radar misc speckle misc maximum likelihood estimation misc image segmentation misc markov processes misc radar imaging misc simulated sar images misc real sar images misc triplet markov field model misc synthetic aperture radar image segmentation misc simple likelihood modelling misc effective optimisation misc unsupervised sar image segmentation algorithm misc complex speckle noise statistical models misc piecewise constant likelihood model misc kernel mapped space misc sar image data misc higher dimension space misc kernel function misc max-product belief propagation algorithm misc kernel tmf model misc Engineering (General). Civil engineering (General)
topic_browse	misc TA1-2040 misc synthetic aperture radar misc speckle misc maximum likelihood estimation misc image segmentation misc markov processes misc radar imaging misc simulated sar images misc real sar images misc triplet markov field model misc synthetic aperture radar image segmentation misc simple likelihood modelling misc effective optimisation misc unsupervised sar image segmentation algorithm misc complex speckle noise statistical models misc piecewise constant likelihood model misc kernel mapped space misc sar image data misc higher dimension space misc kernel function misc max-product belief propagation algorithm misc kernel tmf model misc Engineering (General). Civil engineering (General)
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title	Unsupervised SAR image segmentation based on kernel TMFs with belief propagation
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title_full	Unsupervised SAR image segmentation based on kernel TMFs with belief propagation
author_sort	Lu Gan
journal	The Journal of Engineering
journalStr	The Journal of Engineering
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author_browse	Lu Gan Xiaoming Liu Ziwei Li
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format_se	Elektronische Aufsätze
author-letter	Lu Gan
doi_str_mv	10.1049/joe.2019.0428
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title_sort	unsupervised sar image segmentation based on kernel tmfs with belief propagation
callnumber	TA1-2040
title_auth	Unsupervised SAR image segmentation based on kernel TMFs with belief propagation
abstract	The triplet Markov field (TMF) model has achieved promising results in synthetic aperture radar (SAR) image segmentation. Focusing on the simple likelihood modelling of an SAR image and the effective optimisation of the TMF model, an unsupervised SAR image segmentation algorithm based on kernel TMF with belief propagation is proposed in this study. Instead of complex speckle noise statistical models in a spatial domain, a piecewise constant likelihood model in the kernel mapped space is used in the TMF model by mapping the SAR image data into a higher dimension space via a kernel function. The max-product belief propagation algorithm is used to realise effective optimisation of the proposed kernel TMF model. Experiments on both simulated and real SAR images demonstrate the effectiveness of the proposed algorithm.
abstractGer	The triplet Markov field (TMF) model has achieved promising results in synthetic aperture radar (SAR) image segmentation. Focusing on the simple likelihood modelling of an SAR image and the effective optimisation of the TMF model, an unsupervised SAR image segmentation algorithm based on kernel TMF with belief propagation is proposed in this study. Instead of complex speckle noise statistical models in a spatial domain, a piecewise constant likelihood model in the kernel mapped space is used in the TMF model by mapping the SAR image data into a higher dimension space via a kernel function. The max-product belief propagation algorithm is used to realise effective optimisation of the proposed kernel TMF model. Experiments on both simulated and real SAR images demonstrate the effectiveness of the proposed algorithm.
abstract_unstemmed	The triplet Markov field (TMF) model has achieved promising results in synthetic aperture radar (SAR) image segmentation. Focusing on the simple likelihood modelling of an SAR image and the effective optimisation of the TMF model, an unsupervised SAR image segmentation algorithm based on kernel TMF with belief propagation is proposed in this study. Instead of complex speckle noise statistical models in a spatial domain, a piecewise constant likelihood model in the kernel mapped space is used in the TMF model by mapping the SAR image data into a higher dimension space via a kernel function. The max-product belief propagation algorithm is used to realise effective optimisation of the proposed kernel TMF model. Experiments on both simulated and real SAR images demonstrate the effectiveness of the proposed algorithm.
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title_short	Unsupervised SAR image segmentation based on kernel TMFs with belief propagation
url	https://doi.org/10.1049/joe.2019.0428 https://doaj.org/article/5a97b00fda83408e823520cbff6a31bc https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0428 https://doaj.org/toc/2051-3305
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doi_str	10.1049/joe.2019.0428
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up_date	2024-07-04T01:15:40.804Z
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Unsupervised SAR image segmentation based on kernel TMFs with belief propagation

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