Abstract: In order to solve the problem that the traditional remote sensing imageries change detection method has high quality requirements,low adaptability range and low detection precision,on the basis of introducing the idea of outlier detection,a remote sensing imageries change detection method combined with mean-shift and cluster analysis is proposed in this paper.First,early stage geographic situation vector is used to register with the RS image,and the multi-scale subdivision of the remote sensing imageries is done.The resulting small image inherits the original pattern category attribute,and then extracts the spectral,geometric,texture,and exponential features of the image.And then clustering the extracted multi-feature based on GMM-EM.According to the type of clustering,the change pattern is determined by comparing with the original vector pattern.The experimental results show that the method is effective and reliable,which provides a new idea for remote sensing image change detection.

Key words: mean-shift segmentation, change detection, multiple feature extraction, clustering analysis