This paper presents an evidence theory based change detection method capable of utilizing multiple image features. With a moving window

we first get the structural similarities of both time phase image visual features and construct the basic probability assignment function （BPAF） of D-S evidence theory. We then fuse all the evidence and get the changed image areas with decision rules. Comparative work on different experimental areas

combinations of change evidence and with other meth- ods has been carried out. It shows that our method prevents effectively the detection errors from only utilizing single feature and thus improves the detection precision. Furthermore

since the image similarity is derived from image statistical features rather than original grey

texture and gradient features

this method is robust to low calibration precision.