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In this letter, we propose an unsupervised approach for change detection in multitemporal satellite images based on a novel detail-enhancing algorithm. The multitemporal source images are first used to generate the difference image, which is decomposed into low-pass approximation and high-pass directional subbands by the nonsubsampled contourlet transform. The coefficients from the directional subbands are fused at intrascale and interscale to extract the meaningful details of the difference image. After that, the extracted details are injected into one base image selected from the approximation subbands, which results in a detail-enhanced difference image. For each pixel in the enhanced difference image, a dimension-reduced feature vector is created using the principal component analysis (PCA). The final change detection map is achieved by clustering the feature vectors using a PCA-guided k-means algorithm into “changed” and “unchanged” classes. Experimental results demonstrate the superior performance of the proposed approach compared with several well-known change detection techniques.