Change detection using high spatial resolution remotely sensed im-agery by combining evidence theory and structural similarity
多特征证据融合的遥感图像变化检测

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.