Method of remote sensing images dense matching based on multi-scale features

The algorithm adopting multi-scale features to generate parallax images with dense matching for image sequences collected from unmanned airship platform is proposed. Scale invariant feature transform (SIFT) algorithm is adopted to extract features from adjacent images firstly. The initial matches based on Euclidean distance are carried out, and the efficiency of feature matching is improved by reducing the search area. The random sample consensus (RANSAC) algorithm is adopted to estimate the fundamental matrix, and the epipolar geometry constrain is used to delete the wrong matches to improve the robustness and accuracy of feature matching. The region growing algorithm is adopted to carry out dense matching and produce parallax images. Experimental results indicate that the proposed algorithm keeps robustness, reduces the time complexity, generates lots of dense feature matches and gets good visual effects.