To obtain complementary information of difference image and gain a bet ter change detection result, this paper proposes an unsupervised change detection algorithm.The technique is based on minimum spanning tree (MST) clustering.First of all,a normalized neighborhood ratio approach is adopted to obtain the difference image of two images acquired in the same geographical area at different moments.T hen divide pixels into changed pixels, unchanged pixels and uncertain pixels according to gray difference histogram.Ai ming at uncertain pixels,use minimum spanning tree algorithm twice to clustering :texture features and minimum spanning tree algorithm are used for first clustering,then calculate every clustering′s average wei ght,and regarded first clustering results as nodes to calculate the minimum spanning tree again.Finally,on the b asis of optimal objective function, the uncertain pixels belong to the two clusters:changed pixels and unchanged pixels.In this paper,the effectiveness of the algorithm is proved by numerical calculation.Mex ico data accuracy is 99.01% and operation time is 8.49s,Sardinia data accuracy is 98.62% and operation t ime is 8.49s.Compared with the other three kinds of algorithms,the propo sed algorithm can obtain higher detection accuracy and shorter running time.