In this paper a new texture-based change detection approach is proposed to identify the flooded regions in SAR images. The main novelty of our approach is that the most distinctive texture information is automatically learned from the training set. Forty texture features, which are extracted from a pair of bi-temporal SAR images, are used to construct the weak classifier pool. After AdaBoost training, a strong classifier is optimally combined by a small subset of the candidate weak classifiers. The experimental results demonstrate the effectiveness of the proposed approach.