Abstract: In order to improve the accuracy and robustness of SAR ( Synthetic Aperture Radar) image change detection, an unsupervised SAR image change detection method based on image enhancement and fusion is proposed. In order to obtain better effects of background noise suppression, change region enhancement and edge preservation, the log-ratio and mean-ratio differential image are constructed based on the adaptive image enhancement of the original SAR image. The differential image is fused by the fusion strategy of weighted average of low-frequency wavelet coefficients and selecting high-frequency wavelet coefficients according to the minimum local energy. The experimental results show that the fused differential image combined with fuzzy local information C-means clustering has achieved high detection accuracy and kappa coefficient on different data sets, and has strong robustness. It can be widely used in the field of SAR image change detection. 

Key words:  image change detection')">

 image change detection, image enhancement; image fusion, fuzzy local information C-means clustering, synthetic aperture rada