Synthetic Aperture Radar (SAR) is suitable for dynamic monitoring because it is unaffected by weather conditions. SAR image change detection is the key technology for the dynamic monitoring of targets. However

multi-scale SAR images contain more details than single-scale images. Thus

we integrate multi-scale analysis in the change detection algorithm to obtain accurate results. Gaussian multi-scale theory is easy to understand and has advantages. Despite its capacity to preserve image details

it is seldom used in change detection. This study proposes a new adaptive multi-scale change detection method on the basis of the structure similarity (SSIM) of SAR images. The proposed method includes five steps

namely

obtaining difference images (DIs)

multi-scale decomposition based on Gaussian kernel

optimal scale estimation by SSIM

building feature vectors for each pixel

and fuzzy C-means (FCM) clustering to obtain the final change map. DIs are obtained using a log-ratio operator. A median filter is utilized to suppress the speckle noise. The optimal Gaussian scale is estimated by finding the maximum SSIM of the DIs through iterations. The optimal Gaussian kernel scale and its differential forms are convoluted with the DIs to generate change detection feature vectors at the pixel level. FCM is introduced to classify the changed and unchanged pixels using the feature vectors

and the change detection map is achieved. Experiments on two pairs of real SAR images of Bern and Ottawa areas show that the proposed method outperforms state-of-the-art algorithms. The correct detection rates of the two pairs of SAR images reach 0.9952 and 0.9623

and their kappa coefficients reach 0.8200 and 0.8540. This study proposes an effective multi-scale change detection method based on the SSIM of SAR images. This method fully utilizes the scale information of SAR images and is robust to speckle noise. The proposed method is effective in finding the optimal scale of DI and in detecting changes.