In this research, we propose an unsupervised change detection methodology for synthetic aperture radar (SAR) images. By analyzing the goodness of fit, it is found that pixels in no change area in the change image generated by normalized difference ratio (NOR) operator better fit with normal distribution. Based on this assumption, we identify the pixel range that fits the normal distribution better than any other range iteratively in the image and the range is defines as the threshold value. Finally, a region growing segmentation algorithm was modified to fit for the post processing in change detection. Experiments were carried out with all possible cases of changes: (i) double change, (ii) single change and (iii) no change to prove the effectiveness of the proposed methodology.