Abstract:

In order to solve the problems in the characteristics of UAV(Unmanned Aerial Vehicle) image with large frame, i.e., large rotation angle, large difference in scales an
d color difference, a matching method named multi-scale LSM(Least Squares Matching) algorithm based on SIFT (Scale Invariant Feature Transform) features with epipolar and homography constraints, which can improve the matching success rate is designed. On the top pyramid images, SIFT image matching is done to obtain matching points. The homography matrix and basic matrix are calculated with the matching points by the improved RANSAC(RANdom SAmple Consensus) algorithm. And the harris feature extraction is used to obtain many feature points. According to epipolar and homography constraints two-dimensional concordance correlation coefficient algorithm is used to dense stereo matching. The homography matrix is updated for deleting false matching points by setting threshold. Corresponding image points are used to obtain sub-pixel accuracy by LSM. Based on three groups of comparative tests with actual aerial photograph images, i.e., images with large rotation angle, lager different scales and color difference, it is proved that this method is effective.

Key words: unmanned aerial vehicle (UAV), image matching, scale invariant feature transform(SIFT) feature, random sample consensus(RANSAC), geometrical constraint