Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model

被引：56
|
作者：
Voisin, Aurelie [1 ]
Krylov, Vladimir A. [1 ]
Moser, Gabriele [2 ]
Serpico, Sebastiano B. [2 ]
Zerubia, Josiane [1 ]
机构：
[1] Inst Natl Rech Informat & Automat, Ayin Team, F-06902 Sophia Antipolis, France
[2] Univ Genoa, Dipartimento Ingn & Biofis Elettron, I-16145 Genoa, Italy
来源：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2013年 / 10卷 / 01期
关键词：
Hierarchical Markov random fields (MRFs); supervised classification; synthetic aperture radar (SAR); textural features; urban areas; wavelets;
D O I：
10.1109/LGRS.2012.2193869
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
This letter addresses the problem of classifying synthetic aperture radar (SAR) images of urban areas by using a supervised Bayesian classification method via a contextual hierarchical approach. We develop a bivariate copula-based statistical model that combines amplitude SAR data and textural information, which is then plugged into a hierarchical Markov random field model. The contribution of this letter is thus the development of a novel hierarchical classification approach that uses a quad-tree model based on wavelet decomposition and an innovative statistical model. The performance of the developed approach is illustrated on a high-resolution satellite SAR image of urban areas.
引用
收藏
页码：96 / 100
页数：5
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