Synthetic aperture radar (SAR) change detection identifies differences between co-registered images acquired at different times. While SAR is highly suitable for disaster monitoring due to its all-weather and day-night capability, severe speckle complicates the detection task. We present a hybrid pipeline comprising edge-preserving denoising, fusion of three standard difference maps into a unified indicator, two-cluster Fuzzy C-Means to obtain high-confidence change and unchanged seeds, and a compact three-level Mini-U-Net trained with weighted Binary Cross-Entropy and Dice loss to classify uncertain pixels through sliding-window inference. Across three SAR datasets, the proposed method surpasses recent baselines in overall accuracy, F1 score and intersection over union, achieving effective speckle suppression and accurate change delineation.