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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.
出处 《信息与电子工程前沿:英文版》 SCIE EI CSCD 2019年第2期253-264,共12页 Frontiers of Information Technology & Electronic Engineering
基金 the National Natural Science Foundation of China(No.61331017).
作者简介 Ting-ting JIN,E-mail:jintingting13@mails.ucas.ac.cn;Corresponding author:Xiao-qiang SHE,sxq@mail.ustc.edu.cn.
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