A SIFT Image Match Method with Match-Support Measure for Multi-source Remotely Sensed Images

An automatic image match method based on SIFT features with match-support measure is presented for multi-source remotely sensed images.In order to adjust SIFT match algorithm for multi-source remote sensing images,the match-support measure is introduced for similarity measure.First,a SIFT feature descriptor is built and the points satisfying the minimum Euclidean distance of the candidate matched points between reference image and match image are selecud.Second,we calculate the match-support measure among these candidate matched points respectively.Finally,we employ the relaxation method to discard the false matched point pairs.A stereo of SPOT-5 HRG imagery and aerial image are selected and used for experiment.The empirical results are compared with the results of traditional grey correlation method and conventional SIFT feature match method.The results show that the SIFT feature match method with match-support measure is reliable and efficient for automatic multi-source remotely sensed image match.