JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering 2019 Vol.20 No.2 P.253-264

Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery

Author(s): Ting-ting Jin, Xiao-qiang She, Xiao-lan Qiu, Bin Lei
Affiliation(s): Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China; more
Corresponding email(s): jintingting13@mails.ucas.ac.cn, sxq@mail.ustc.edu.cn
Key Words: Intertidal classification, Polarimetric synthetic aperture radar, Finite mixture model, Markov random field, Generalized extreme value model

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Abstract: 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.

基于广义极值分布和马尔科夫随机场的全极化合成孔径雷达图像潮间带区域地物分类方法

摘要：考虑到潮汐波动的复杂性和剧烈性，合成孔径雷达（SAR）图像的潮间带区域地物分类是一个重要且具有挑战性的问题。潮间带区域地物分类难点在于，潮间带有很大比例区域经常被海水淹没，使得利用空间信息的统计建模方法常常失效。考虑到极化熵和各向异性在潮间带特征描述中的重要作用，提出一种新的无监督地物分类算法。该算法将广义极值分布模型（GEV）的极化特征与马尔可夫随机场（MRF）的平滑滤波结合，并加入拟合优度检验，以确定统计模型各组成部分的显著性。将极化熵和各向异性的分类结果有效结合，得到最终分类结果。最后，基于中国高分三号SAR卫星获得的全极化数据进行实验，实验结果证明所提方法的可行性和优越性。

关键词：潮间带区域地物分类；极化合成孔径雷达；有限混合模型；马尔可夫随机场；广义极值分布模型

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基于广义极值分布和马尔科夫随机场的全极化合成孔径雷达图像潮间带区域地物分类方法

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