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

页码：350 / 353

页数：4

共 20 条

[1] M-estimators with asymmetric influence functions:: the GA0 distribution case [J].