Unsupervised Classification of SAR Images Using Markov Random Fields and GI0 Model

被引：18
|
作者：
Picco, Mery [1 ]
Palacio, Gabriela [1 ]
机构：
[1] Univ Nacl Rio Cuarto, RA-5800 Rio Cuarto, Argentina
来源：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2011年 / 8卷 / 02期
关键词：
Classification; Markovian segmentation; statistical model; synthetic aperture radar (SAR); DISTRIBUTIONS; ESTIMATORS;
D O I：
10.1109/LGRS.2010.2073678
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 deals with synthetic aperture radar (SAR) data classification in an unsupervised way. Many models have been proposed to fit SAR data (K, Weibull, Log-normal, etc.), but none of them are flexible enough to model all kinds of surfaces (particularly when there are urban areas present in the image). Our main contribution is the application of a statistical model G(0) in a classification process which is shown to be able to model areas with different degrees of heterogeneity. The quality of the classification obtained by mixing this model and a Markovian segmentation is high. We use an iterative conditional estimation method to estimate the parameters of the proposed model.
引用
收藏
页码：350 / 353
页数：4
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