In this study, a method for unsupervised change detection in multi-temporal SAR images has been presented based on integrating clustering and active contour model. In this method, texture information is extracted by using Gabor filter in different scales and directions. KPCA transformation is also applied to reduce the dependency between the extracted features and image information. Moreover, Discrete Wavelet Transformation (DWT) and Gustafson-Kessel clustering (GKC) methods are used respectively to generate the difference image and the initial contour for the active contour model. In the final step, the region-based non-parametric active contour model is used for producing the change image containing changed and unchanged regions. For performance evaluation of the proposed method, two sets of high resolution multi-temporal TerraSAR-X images are considered. Experimental results of unsupervised change detection method show that,  the total error rate of the proposed approach for the first data set are decreased respectively to 4.95%, 3.30% and 3.34% compared to that of the  Chan-Vese, MRF and EMMRF methods and for the second data set, the total error rate of the proposed method are decreased to 2.56%, 1.86% and 1.87 As well. Moreover, the results showed that the use of GKC method leads to production of the initial curve with minimal convergence time for the active contour model. Also, the use of active contour model improves the accuracy of change map creation using a repititive process.