CHANGE DETECTION IN URBAN AREAS WITH HIGH RESOLUTION SAR IMAGES USING SECOND KIND STATISTICS BASED G0 DISTRIBUTION

This paper presents a SAR image change detection algorithm for urban areas which mainly contains three steps: (i) modeling the Synthetic Aperture Radar (SAR) image by G0 distribution; (ii) generating a change map by computing the Kullback-Leibler divergence; (iii) masking the change map to obtain the final change areas. We propose to use the second kind statistics based parameter estimation method via Mellin transform to figure out the three parameters of G0 distribution. Experiment results indicate the second kind based G0 distribution model outperforms the moment based methods and can achieve a satisfactory result.