An improved unsupervised classification scheme for polarimetric SAR image with MCSM-Wishart

doi:10.6046/gtzyyg.2015.02.03

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Abstract

To tackle the problems of insufficiently extracting polarimetric information from PolSAR image and low classification accuracy of H/Alpha/A-Wishart unsupervised classification algorithm, this paper proposes an adapted algorithm named MCSM-Wishart by imposing multiple-component scattering model (MCSM)decomposition to fit unsupervised classification of polarimetric SAR image. Firstly, various kinds of polarimetric information such as volume scatter, double scatter, helix scatter, surface scatter and wire scatter can be extracted from the image by MCSM decomposition, and iterative self-organizing data analysis(ISODATA)technique is used for clustering. Then iterative classification based on complex Wishart distribution is used to obtain the final result. H/Alpha-Wishart, H/Alpha/A-Wishart, MCSM-Wishart and supervised-Wishart algorithms are compared with each other based on two research plots conducted respectively in Lishui of Nanjing City and Binhai Wetland of Yancheng City with PALSAR image from ALOS. The results show that MCSM-Wishart classification algorithm can improve to a certain extent the original classifiers in terms of efficiency, total accuracy and Kappa coefficient. It is therefore concluded that the polarimetric information extracted by MCSM decomposition can sufficiently reflect the characteristics of the ground object. Combining with ISODATA clustering algorithm, MCSM decomposition can be used in the iterative classification based on complex Wishart distribution so as to improve the classification accuracy and reliability efficiently.

Keywords chlorophyll-a concentration suspended solids concentration transparency eutrophication index

TP751.1

Issue Date: 02 March 2015

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	ZHU Li
	LI Yunmei
	ZHAO Shaohua
	GUO Yulong

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ZHU Li,LI Yunmei,ZHAO Shaohua, et al. An improved unsupervised classification scheme for polarimetric SAR image with MCSM-Wishart[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 15-21.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.02.03 OR https://www.gtzyyg.com/EN/Y2015/V27/I2/15

[1] Cloude S R,Pottier E.A review of target decomposition theorems in Radar polarimetry[J].IEEE Transactions on Geoscience and Remote Sensing,1996,34(2):498-518.

[2] Cloude S R,Pottier E.An entropy based classification scheme for land applications of polarimetric SAR[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(1):68-78.

[3] Freeman A,Durden S L.A three-component scattering model for polarimetric SAR data[J].IEEE Transactions on Geoscience and Remote Sensing,1998,36(3):963-973.

[4] Yamaguchi Y,Moriyama T,Ishido M,et al.Four-component scattering model for polarimetric SAR image decomposition[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(8):1699-1706.

[5] Yamaguchi Y,Sato A,Boerner W M,et al.Four-component scattering power decomposition with rotation of coherency matrix[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(6):2251-2258.

[6] Zhang L M,Zou B,Cai H J,et al.Multiple-component scattering model for polarimetric SAR image decomposition[J].IEEE Geoscience and Remote Sensing Letters,2008,5(4):603-607.

[7] 王庆,曾琪明,廖静娟.基于极化分解的极化特征参数提取与应用[J].国土资源遥感,2012,24(3):103-110.doi:10.6046/gtzyyg.2012.03.19. Wang Q,Zeng Q M,Liao J J.Extraction and application of polarimetric characteristic parameters based on polarimetric decomposition[J].Remote Sensing for Land and Resources,2012,24(3):103-110.doi:10.6046/gtzyyg.2012.03.19.

[8] Lee J S,Grunes M R,Ainsworth T L,et al.Unsupervised classification using polarimetric decomposition and the complex Wishart classifier[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(5):2249-2258.

[9] Pottier E,Lee J S.Application of the H/A/α polarimetric decomposition theorem for unsupervised classification of fully polarimetric SAR data based on the Wishart distribution[J].Proceeding of the Committee on Earth Observing Satellites SAR Workshop.Toulouse:NASA,1999:335-340.

[10] Lee J S,Grunes M R,Pottier E,et al.Unsupervised terrain classification preserving polarimetric scattering characteristics[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(4):722-731.

[11] Cao F,Hong W,Wu Y R.An improved Cloude-Pottier decomposition using H/α/SPAN and complex Wishart classifier for polarimetric SAR classification[C]//International Conference on Radar.Shanghai:IEEE,2006:1-4.

[12] 杨磊,刘伟,王志刚.加权全极化SAR图像非监督Wishart分类方法[J].电子与信息学报,2008,30(12):2827-2830. Yang L,Liu W,Wang Z G.Weighted-based unsupervised Wishart classification of fully polarimetric SAR image[J].Journal of Electronics and Information Technology,2008,30(12):2827-2830.

[13] 郎丰铠,杨杰,赵伶俐,等.基于Freeman散射熵和各向异性度的极化SAR影像分类算法研究[J].测绘学报,2012,41(4):556-562. Lang F K,Yang J,Zhao L L,et al.Polarimetric SAR data classification with freeman entropy and anisotropy analysis[J].Acta Geodaetica et Cartographica Sinica,2012,41(4):556-562.

[14] 赵伶俐,杨杰,李平湘,等.极化SAR影像弱散射地物统计分类[J].遥感学报,2013,17(2):312-310. Zhao L L,Yang J,Li P X,et al.Statistical classification of weak backscattering scatterers of PolSAR image[J].Journal of Remote Sensing,2013,17(2):312-319.

[15] Paladini R,Martorella M,Berizzi F.Classification of man-made targets via invariant coherency-matrix eigenvector decomposition of polarimetric SAR/ISAR images[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(8):3022-3034.

[16] Zhang L M,Lu D,Tang W Y.A GS-based built-up area detection method using polarimetric SAR images[C]//2012 IEEE International Geoscience and Remote Sensing Symposium.Munich:IEEE,2012:5911-5914.

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