Fully Statistical, Wavelet-based conditional random field (FSWCRF) for SAR image segmentation

•A new statistical conditional random field is proposed for SAR image segmentation.•The generalized Gaussian distribution of wavelet coefficients is applied in the CRF.•The unary potential is constructed based on the generalized Gaussian distribution.•The Kullback–Leibler distance improves the pairw...

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Vydáno v:	Expert systems with applications Ročník 168; s. 114370
Hlavní autoři:	Golpardaz, Maryam, Helfroush, Mohammad Sadegh, Danyali, Habibollah, Ghaffari, Reyhane
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Elsevier Ltd 15.04.2021 Elsevier BV
Témata:	Algorithms Conditional Random Field (CRF) Conditional random fields Distribution functions Feature extraction Generalized Gaussian Distributions (GGD) Image segmentation Kullback–Leibler Distance (KLD) Normal distribution Parameters Radar imaging Regression models Similarity Similarity measures Statistical analysis Synthetic aperture radar Synthetic Aperture Radar (SAR) image segmentation Two dimensional models Wavelet transforms Conditional Random Field (CRF) Generalized Gaussian Distributions (GGD) Synthetic Aperture Radar (SAR) image segmentation Kullback–Leibler Distance (KLD)
ISSN:	0957-4174, 1873-6793
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Abstract	•A new statistical conditional random field is proposed for SAR image segmentation.•The generalized Gaussian distribution of wavelet coefficients is applied in the CRF.•The unary potential is constructed based on the generalized Gaussian distribution.•The Kullback–Leibler distance improves the pairwise potential results. Recently, the conditional random field (CRF) model has been greatly considered in synthetic aperture radar (SAR) image segmentation. This model not only directly considers the posterior distribution of the label field conditioned on images but also gives the interactions between the observations. In this paper, we propose a new CRF-based algorithm for SAR image segmentation. We consider the statistical approach jointly in feature extraction and similarity measurement in the proposed conditional random field model. Using the benefit of the 2-D wavelet transform, we define the generalized Gaussian distribution (GGD) on the wavelet coefficients to extract texture-based features. Then, to improve the CRF potential functions a new unary function is proposed which exactly matches the statistical properties of the wavelet coefficients and produces more accurate parameters for different regions. As the advantage of this function, it is no longer necessary to apply the multinomial logistic regression (MLR) model used in previous CRFs. Moreover, using the Kullback–Leibler distance (KLD) between distribution functions, the similarity measure in our pairwise potential is proposed very effectively and efficiently. The superiority of this scheme is that the similarity measure can be entirely computed using the parameters of the GGD that are typically of small size compared with the feature vectors in the previous methods. Comprehensive experiments on both synthetic and real SAR images indicate that our proposed algorithm achieves accuracy improvement in SAR image segmentation.
AbstractList	Recently, the conditional random field (CRF) model has been greatly considered in synthetic aperture radar (SAR) image segmentation. This model not only directly considers the posterior distribution of the label field conditioned on images but also gives the interactions between the observations. In this paper, we propose a new CRF-based algorithm for SAR image segmentation. We consider the statistical approach jointly in feature extraction and similarity measurement in the proposed conditional random field model. Using the benefit of the 2-D wavelet transform, we define the generalized Gaussian distribution (GGD) on the wavelet coefficients to extract texture-based features. Then, to improve the CRF potential functions a new unary function is proposed which exactly matches the statistical properties of the wavelet coefficients and produces more accurate parameters for different regions. As the advantage of this function, it is no longer necessary to apply the multinomial logistic regression (MLR) model used in previous CRFs. Moreover, using the Kullback–Leibler distance (KLD) between distribution functions, the similarity measure in our pairwise potential is proposed very effectively and efficiently. The superiority of this scheme is that the similarity measure can be entirely computed using the parameters of the GGD that are typically of small size compared with the feature vectors in the previous methods. Comprehensive experiments on both synthetic and real SAR images indicate that our proposed algorithm achieves accuracy improvement in SAR image segmentation. •A new statistical conditional random field is proposed for SAR image segmentation.•The generalized Gaussian distribution of wavelet coefficients is applied in the CRF.•The unary potential is constructed based on the generalized Gaussian distribution.•The Kullback–Leibler distance improves the pairwise potential results. Recently, the conditional random field (CRF) model has been greatly considered in synthetic aperture radar (SAR) image segmentation. This model not only directly considers the posterior distribution of the label field conditioned on images but also gives the interactions between the observations. In this paper, we propose a new CRF-based algorithm for SAR image segmentation. We consider the statistical approach jointly in feature extraction and similarity measurement in the proposed conditional random field model. Using the benefit of the 2-D wavelet transform, we define the generalized Gaussian distribution (GGD) on the wavelet coefficients to extract texture-based features. Then, to improve the CRF potential functions a new unary function is proposed which exactly matches the statistical properties of the wavelet coefficients and produces more accurate parameters for different regions. As the advantage of this function, it is no longer necessary to apply the multinomial logistic regression (MLR) model used in previous CRFs. Moreover, using the Kullback–Leibler distance (KLD) between distribution functions, the similarity measure in our pairwise potential is proposed very effectively and efficiently. The superiority of this scheme is that the similarity measure can be entirely computed using the parameters of the GGD that are typically of small size compared with the feature vectors in the previous methods. Comprehensive experiments on both synthetic and real SAR images indicate that our proposed algorithm achieves accuracy improvement in SAR image segmentation.
ArticleNumber	114370
Author	Golpardaz, Maryam Ghaffari, Reyhane Danyali, Habibollah Helfroush, Mohammad Sadegh
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Cites_doi	10.3390/app8122576 10.1109/TPAMI.2008.105 10.1117/1.JRS.12.045006 10.3969/j.issn.1004-4132.2010.01.006 10.1109/TGRS.2007.907109 10.1109/TGRS.2016.2611060 10.1109/83.753747 10.3390/rs11212462 10.1109/MGRS.2017.2762307 10.3390/rs10060906 10.1109/JSTARS.2015.2492552 10.1109/GlobalSIP.2017.8308643 10.1080/01621459.1987.10478393 10.1109/TGRS.2012.2194787 10.1109/ACCESS.2019.2912174 10.1109/TGRS.2015.2413905 10.1016/j.patcog.2016.11.015 10.1080/0143116021000013322 10.1109/IKT.2015.7288780 10.1016/j.sigpro.2020.107623 10.1109/LGRS.2015.2478256 10.1109/TFUZZ.2018.2796074 10.1109/83.869185 10.1016/j.patrec.2016.03.032 10.1080/01431161.2019.1706202 10.3390/rs11050512 10.1109/TGRS.2013.2287273 10.1080/01431161.2016.1266104
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Keywords	Conditional Random Field (CRF) Generalized Gaussian Distributions (GGD) Synthetic Aperture Radar (SAR) image segmentation Kullback–Leibler Distance (KLD)
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Snippet	•A new statistical conditional random field is proposed for SAR image segmentation.•The generalized Gaussian distribution of wavelet coefficients is applied in... Recently, the conditional random field (CRF) model has been greatly considered in synthetic aperture radar (SAR) image segmentation. This model not only...
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SubjectTerms	Algorithms Conditional Random Field (CRF) Conditional random fields Distribution functions Feature extraction Generalized Gaussian Distributions (GGD) Image segmentation Kullback–Leibler Distance (KLD) Normal distribution Parameters Radar imaging Regression models Similarity Similarity measures Statistical analysis Synthetic aperture radar Synthetic Aperture Radar (SAR) image segmentation Two dimensional models Wavelet transforms
Title	Fully Statistical, Wavelet-based conditional random field (FSWCRF) for SAR image segmentation
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