Statistical Wavelet Subband Modeling for Multi-Temporal SAR Change Detection

In the context of multi-temporal SAR change detection for earth monitoring applications, one critical issue is to generate accurate change map. A common method to generate change map is to apply logarithm to the ratio image. However, due to the speckle effect and without consideration of contextual information, it is usually not efficient for accurate change detection. In this paper, an unsupervised change detection method in wavelet domain based on statistical wavelet subband modeling is proposed. The motivation is to capture textures efficiently in wavelet domain. Wavelet transform is applied to decompose the image into multiple scales and probability density function of the coefficient magnitudes of each subband assumed to be Generalized Gaussian Distribution (GGD) and Generalized Gamma Distribution (G) are obtained by fast parameter estimation. Closed-form expression of Kullback-Leibler divergence between two corresponding subbands of the same scale is computed and used to generate the change map. This approach is comprehensively evaluated and compared using different parameter setting, different scales, window sizes and estimators. The proposed SAR change detection in wavelet domain shows promising results as texture can be better characterized in wavelet domain than in spatial domain. Through this study, we conclude that the accuracy depends heavily on the estimation methods although the model is important. Both parameter estimation for GGD based on shape equation and parameter estimation for G using method of log-cumulants (MoLC) in wavelet domain performs quite well.