Bivariate Gamma Distributions for Image Registration and Change Detection

Florent Chatelain (1) , Jean-Yves Tourneret (1, 2) , Jordi Inglada (3) , André Ferrari (4)

1 IRIT-SC - Signal et Communications
2 Toulouse INP - Institut National Polytechnique (Toulouse)
3 CNES - Centre National d'Études Spatiales [Toulouse]
4 FIZEAU - Laboratoire Hippolyte Fizeau

This paper evaluates the potential interest of using bivariate gamma distributions for image registration and change detection. The first part of this paper studies estimators for the parameters of bivariate gamma distributions based on the maximum likelihood principle and the method of moments. The performance of both methods are compared in terms of estimated mean square errors and theoretical asymptotic variances. The mutual information is a classical similarity measure which can be used for image registration or change detection. The second part of the paper studies some properties of the mutual information for bivariate Gamma distributions. Image registration and change detection techniques based on bivariate gamma distributions are finally investigated. Simulation results conducted on synthetic and real data are very encouraging. Bivariate gamma distributions are good candidates allowing us to develop new image registration algorithms and new change detectors.

Keywords: Mutual information; Multivariate gamma distributions; Maximum likelihood; Image change detection; Image registration; Correlation coefficient

Domains: Signal and Image Processing; Signal and Image processing

Florent Chatelain, Jean-Yves Tourneret, Jordi Inglada, André Ferrari. Bivariate Gamma Distributions for Image Registration and Change Detection. IEEE Transactions on Image Processing, 2007, 16 (7), pp.1796-1806. ⟨10.1109/TIP.2007.896651⟩. ⟨hal-00474882⟩