Change detection in multisensor SAR images using bivariate gamma distributions

被引：87

作者：

Chatelain, Florent ^{[1
]}

Tourneret, Jean-Yves ^{[1
]}

Inglada, Jordi ^{[2
]}

机构：

[1] Univ Toulouse 3, IRIT, ENSEEIHT, TeSA, F-31071 Toulouse, France

[2] CNES, F-31401 Toulouse 9, France

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 2008年 / 17卷 / 03期

关键词：

change detection; correlation coefficient; maximum likelihood; multivariate gamma distributions;

D O I：

10.1109/TIP.2008.916047

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies a family of distributions constructed from multivariate gamma distributions to model the statistical properties of multisensor synthetic aperture radar (SAR) images. These distributions referred to as multisensor multivariate gamma distributions (MuMGDs) are potentially interesting for detecting changes in SAR images acquired by different sensors having different numbers of looks. The first part of this paper compares different estimators for the parameters of MuMGDs. These estimators are based on the maximum likelihood principle, the method of inference function for margins, and the method of moments. The second part of the paper studies change detection algorithms based on the estimated correlation coefficient of MuMGDs. Simulation results conducted on synthetic and real data illustrate the performance of these change detectors.

引用

页码：249 / 258

页数：10