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Change detection in multisensor SAR images using bivariate gamma distributions.

F Chatelain¹, J-Y Tourneret, J Inglada

¹IRIT/ENSEEIHT/TéSA, 31071 Toulouse cedex 7, France. florent.chatelain@enseeiht.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 14, 2008

Summary

This study introduces multisensor multivariate gamma distributions (MuMGDs) for analyzing synthetic aperture radar (SAR) images from different sensors. The research compares parameter estimation methods and develops change detection algorithms for improved SAR image analysis.

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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

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Area of Science:

Statistical modeling
Remote sensing image analysis

Background:

Synthetic aperture radar (SAR) images from multiple sensors present unique statistical properties.
Modeling these properties is crucial for effective change detection.

Purpose of the Study:

To introduce and analyze multisensor multivariate gamma distributions (MuMGDs) for multisensor SAR image modeling.
To compare parameter estimation techniques for MuMGDs.
To develop and evaluate change detection algorithms utilizing MuMGDs.

Main Methods:

Construction of multisensor multivariate gamma distributions (MuMGDs).
Comparison of parameter estimation methods: Maximum Likelihood, Inference Function for Margins, and Method of Moments.
Development of change detection algorithms based on the estimated correlation coefficient of MuMGDs.

Main Results:

Performance evaluation of different parameter estimators for MuMGDs.
Demonstration of change detection algorithm effectiveness using synthetic and real SAR data.
Insights into the statistical properties of multisensor SAR images.

Conclusions:

MuMGDs provide a suitable framework for modeling multisensor SAR image statistics.
The study offers a comparative analysis of estimation methods and effective change detection strategies for multisensor SAR data.