Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery
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摘要
Classification of intertidal area in synthetic aperture radar(SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value(GEV) statistical model of the polarization features and the Markov random field(MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.
Classification of intertidal area in synthetic aperture radar(SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value(GEV) statistical model of the polarization features and the Markov random field(MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.
Classification of intertidal area in synthetic aperture radar(SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value(GEV) statistical model of the polarization features and the Markov random field(MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.
引文
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Boerner WM,1990.Introduction to radar polarimetry-with assessments of the historical development and of the current state-of-the-art.In:Guran AS,Mittra R,Moser PJ(Eds.),Electromagnetic Wave Interactions.World Scientific,Singapore,p.139-214.https://doi.org/10.1142/9789812813091_0004
Cloude SR,1995.An entropy based classification scheme for polarimetric SAR data.Int Geoscience and Remote Sensing Symp,p.2000-2002.https://doi.org/10.1109/IGARSS.1995.524090
Cloude SR,Pottier E,1996.A review of target decomposition theorems in radar polarimetry.IEEE Trans Geosci Remote Sens,34(2):498-518.https://doi.org/10.1109/36.485127
Ding H,Huang Y,Liu N,et al.,2015.Modeling of sea spike events with generalized extreme value distribution.European Radar Conf,p.113-116.https://doi.org/10.1109/EuRAD.2015.7346250
Doulgeris AP,Akbari V,Eltoft T,2012.Automatic Pol SARsegmentation with the?-distribution and Markov random fields.European Conf on Synthetic Aperture Radar,p.183-186.
Geng XM,Li XM,Velotto D,et al.,2016.Study of the polarimetric characteristics of mud flats in an intertidal zone using C-and X-band spaceborne SAR data.Remote Sens Environ,176:56-68.https://doi.org/10.1016/j.rse.2016.01.009
Inglada J,Garello R,2000.Underwater bottom topography estimation from SAR images by regularization of the inverse imaging mechanism.Proc IEEE Int Geoscience and Remote Sensing Symp,p.1848-1850.https://doi.org/10.1109/IGARSS.2000.858143
Kim DJ,Park SE,Lee HS,et al.,2009.Investigation of multiple frequency polarimetric SAR signal backscattering from tidal flats.IEEE Int Geoscience and Remote Sensing Symp,p.896-899.https://doi.org/10.1109/IGARSS.2009.5417916
Kim JE,Park SE,Kim DJ,et al.,2011.Recent advances in POL(in)SAR remote sensing and stress-change monitoring of wetlands with applications to the Sunchon Bay Tidal Flats.European Conf on Synthetic Aperture Radar,p.1-3.
Lee H,Chae H,Cho SJ,2011.Radar backscattering of intertidal mudflats observed by Radarsat-1 SAR images and ground-based scatterometer experiments.IEEE Trans Geosci Remote Sens,49(5):1701-1711.https://doi.org/10.1109/TGRS.2010.2084094
Li HC,Hong W,Wu YR,2007.Generalized gamma distribution with MoLC estimation for statistical modeling of SAR images.1st Asian and Pacific Conf on Synthetic Aperture Radar,p.525-528.https://doi.org/10.1109/APSAR.2007.4418665
Li SZ,2009.Markov Random Field Modeling in Image Analysis.Springer,London,UK.https://doi.org/10.1007/978-1-84800-279-1
Li Z,Heygester G,Notholt J,2012.Topographic mapping of Wadden Sea,with SAR images and waterlevel model data.IEEE Int Geoscience and Remote Sensing Symp,p.2645-2648.https://doi.org/10.1109/IGARSS.2012.6350385
Li Z,Heygester G,Notholt J,2013.The topography comparsion between the year 1999 and 2006 of German tidal flat wadden sea analyzing SAR images with waterline method.IEEE Int Geoscience and Remote Sensing Symp,p.2443-2446.https://doi.org/10.1109/IGARSS.2013.6723314
Li Z,Heygster G,Notholt J,2014.Intertidal topographic maps and morphological changes in the German wadden sea between 1996-1999 and 2006-2009 from the waterline method and SAR images.IEEE J Sel Top Appl Earth Observ Remote Sens,7(8):3210-3224.https://doi.org/10.1109/JSTARS.2014.2313062
Park SE,Moon WM,Kim DJ,2009.Estimation of surface roughness parameter in intertidal mudflat using airborne polarimetric SAR data.IEEE Trans Geosci Remote Sens,47(4):1022-1031.https://doi.org/10.1109/TGRS.2008.2008908
Peli E,1990.Contrast in complex images.J Opt Soc Am A,7(10):2023-2040.https://doi.org/10.1364/JOSAA.7.002032
Pottier E,1998.Unsupervised classification scheme and topography derivation of POLSAR data on the H/A/a polarimetric decomposition theorem.Proc 4th Int Workshop on Radar Polarimetry,p.535-548.
She X,Qiu X,Lei B,et al.,2017.A classification method based on polarimetric entropy and GEV mixture model for intertidal area of PolSAR image.J Rad,6(5):554-563(in Chinese).https://doi.org/10.12000/JR16149
Uebersax JS,1982.A generalized Kappa coefficient.Educ Psychol Meas Sage Publ,42(1):181-183.https://doi.org/10.1177/0013164482421018
van del Wal D,Herman PMJ,van den Dool AW,2005.Characterisation of surface roughness and sediment texture of intertidal flats using ERS SAR imagery.Remote Sens Environ,98(1):96-109.https://doi.org/10.1016/j.rse.2005.06.004https://doi.org/10.1109/IGARSS.2011.6049578
Won ES,Ouchi K,Yang CS,2013.Extraction of underwater laver cultivation nets by SAR polarimetric entropy.IEEEGeosci Remote Sens Lett,10(2):231-235.https://doi.org/10.1109/LGRS.2012.2199077
Wu Y,Ji K,Yu W,et al.,2008.Region-based classification of polarimetric SAR images using Wishart MRF.IEEEGeosci Remote Sens Lett,5(4):668-672.https://doi.org/10.1109/LGRS.2008.2002263
Boerner WM,1990.Introduction to radar polarimetry-with assessments of the historical development and of the current state-of-the-art.In:Guran AS,Mittra R,Moser PJ(Eds.),Electromagnetic Wave Interactions.World Scientific,Singapore,p.139-214.https://doi.org/10.1142/9789812813091_0004
Cloude SR,1995.An entropy based classification scheme for polarimetric SAR data.Int Geoscience and Remote Sensing Symp,p.2000-2002.https://doi.org/10.1109/IGARSS.1995.524090
Cloude SR,Pottier E,1996.A review of target decomposition theorems in radar polarimetry.IEEE Trans Geosci Remote Sens,34(2):498-518.https://doi.org/10.1109/36.485127
Ding H,Huang Y,Liu N,et al.,2015.Modeling of sea spike events with generalized extreme value distribution.European Radar Conf,p.113-116.https://doi.org/10.1109/EuRAD.2015.7346250
Doulgeris AP,Akbari V,Eltoft T,2012.Automatic Pol SARsegmentation with the?-distribution and Markov random fields.European Conf on Synthetic Aperture Radar,p.183-186.
Geng XM,Li XM,Velotto D,et al.,2016.Study of the polarimetric characteristics of mud flats in an intertidal zone using C-and X-band spaceborne SAR data.Remote Sens Environ,176:56-68.https://doi.org/10.1016/j.rse.2016.01.009
Inglada J,Garello R,2000.Underwater bottom topography estimation from SAR images by regularization of the inverse imaging mechanism.Proc IEEE Int Geoscience and Remote Sensing Symp,p.1848-1850.https://doi.org/10.1109/IGARSS.2000.858143
Kim DJ,Park SE,Lee HS,et al.,2009.Investigation of multiple frequency polarimetric SAR signal backscattering from tidal flats.IEEE Int Geoscience and Remote Sensing Symp,p.896-899.https://doi.org/10.1109/IGARSS.2009.5417916
Kim JE,Park SE,Kim DJ,et al.,2011.Recent advances in POL(in)SAR remote sensing and stress-change monitoring of wetlands with applications to the Sunchon Bay Tidal Flats.European Conf on Synthetic Aperture Radar,p.1-3.
Lee H,Chae H,Cho SJ,2011.Radar backscattering of intertidal mudflats observed by Radarsat-1 SAR images and ground-based scatterometer experiments.IEEE Trans Geosci Remote Sens,49(5):1701-1711.https://doi.org/10.1109/TGRS.2010.2084094
Li HC,Hong W,Wu YR,2007.Generalized gamma distribution with MoLC estimation for statistical modeling of SAR images.1st Asian and Pacific Conf on Synthetic Aperture Radar,p.525-528.https://doi.org/10.1109/APSAR.2007.4418665
Li SZ,2009.Markov Random Field Modeling in Image Analysis.Springer,London,UK.https://doi.org/10.1007/978-1-84800-279-1
Li Z,Heygester G,Notholt J,2012.Topographic mapping of Wadden Sea,with SAR images and waterlevel model data.IEEE Int Geoscience and Remote Sensing Symp,p.2645-2648.https://doi.org/10.1109/IGARSS.2012.6350385
Li Z,Heygester G,Notholt J,2013.The topography comparsion between the year 1999 and 2006 of German tidal flat wadden sea analyzing SAR images with waterline method.IEEE Int Geoscience and Remote Sensing Symp,p.2443-2446.https://doi.org/10.1109/IGARSS.2013.6723314
Li Z,Heygster G,Notholt J,2014.Intertidal topographic maps and morphological changes in the German wadden sea between 1996-1999 and 2006-2009 from the waterline method and SAR images.IEEE J Sel Top Appl Earth Observ Remote Sens,7(8):3210-3224.https://doi.org/10.1109/JSTARS.2014.2313062
Park SE,Moon WM,Kim DJ,2009.Estimation of surface roughness parameter in intertidal mudflat using airborne polarimetric SAR data.IEEE Trans Geosci Remote Sens,47(4):1022-1031.https://doi.org/10.1109/TGRS.2008.2008908
Peli E,1990.Contrast in complex images.J Opt Soc Am A,7(10):2023-2040.https://doi.org/10.1364/JOSAA.7.002032
Pottier E,1998.Unsupervised classification scheme and topography derivation of POLSAR data on the H/A/a polarimetric decomposition theorem.Proc 4th Int Workshop on Radar Polarimetry,p.535-548.
She X,Qiu X,Lei B,et al.,2017.A classification method based on polarimetric entropy and GEV mixture model for intertidal area of PolSAR image.J Rad,6(5):554-563(in Chinese).https://doi.org/10.12000/JR16149
Uebersax JS,1982.A generalized Kappa coefficient.Educ Psychol Meas Sage Publ,42(1):181-183.https://doi.org/10.1177/0013164482421018
van del Wal D,Herman PMJ,van den Dool AW,2005.Characterisation of surface roughness and sediment texture of intertidal flats using ERS SAR imagery.Remote Sens Environ,98(1):96-109.https://doi.org/10.1016/j.rse.2005.06.004https://doi.org/10.1109/IGARSS.2011.6049578
Won ES,Ouchi K,Yang CS,2013.Extraction of underwater laver cultivation nets by SAR polarimetric entropy.IEEEGeosci Remote Sens Lett,10(2):231-235.https://doi.org/10.1109/LGRS.2012.2199077
Wu Y,Ji K,Yu W,et al.,2008.Region-based classification of polarimetric SAR images using Wishart MRF.IEEEGeosci Remote Sens Lett,5(4):668-672.https://doi.org/10.1109/LGRS.2008.2002263