Abstract: In the context of change detection in SAR images, most of techniques are based on the analysis of the difference image, while almost all make the decision at the pixel level. This decision way will cause the change detection map noisy, with holes in connected component and jagged boundaries. For avoiding this, a technique making the decision at the region level is proposed, whose core is extracting and handling region of interest (ROI) and the key is obtaining the proper the label used to guide for extracting ROI and that how to make the result generated at the region level. To make the extracted ROI contain nearly all pixels in the changed area, stationary wavelet transform (SWT) and fuzzy c-means (FCM) algorithm have been used to get it; to make the change detection map generated at the region level, all connected regions in the extracted ROI are searched and each one is looked at as a unit to be handled. Then the threshold-based method is used to group those units into two classes. Results tested on the real SAR datasets have shown that our method outperforms other related techniques, whatever from quantitative or subjective aspects.