Pairwise likelihood estimation for multivariate mixed Poisson models generated by Gamma intensities
Statistics and Computing
Application of DSM theory for SAR image change detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Parallel unsupervised Synthetic Aperture Radar image change detection on a graphics processing unit
International Journal of High Performance Computing Applications
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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.