Change detection is useful for monitoring environmental evolution or tracking anthropogenic changes over time. One of the promotion of satellite monitoring is advocated for its ability to revisit the same sites periodically and gives us a growth data from different sensors like Radars. Synthetic Aperture Radar (SAR) data is preferred because weather conditions (e.g. cloud cover) do not hamper the ability to perform soil measurements. However, traditional change detection methods are sensitive to speckle noise. We propose to compare several statistical change detection methods in order to reduce false alarm. In this paper, the data used are polarimetric and multilook which are simulated by their covariance matrices following a complex Wishart law. Change detection methods rely on the pixel patches whose size we fix. After estimating the parameters of the Wishart law on patches of pixels from the same area but acquired on two different dates, we compare these two patches of pixels with three stochastic divergences: Kullback-Leibler, Bhattacharyya, and Hellinger. The decision threshold is then analyzed by comparing two methods, one by the Otsu segmentation method, the other by the Chi-square distribution. We evaluate these different methods by the number of true positives, false positives, and the number of targets identified. On simulation data, Bhattacharyya distance with the Chi-square threshold gives a higher true positive rate and a lower false alarm rate than other methods. Moreover, we note that the smaller the size of the pixel patches, the more the number of detected targets increases, but with a greater number of false alarms. Due to the strong influence of speckle, which causes false alarms, a post-processing method based on the concentration index of Getis and Franklin is proposed.