IMPROVING THE UAV-DERIVED DSM BY INTRODUCING A MODIFIED RANSAC ALGORITHM
Keywords: UAV image matching, UAV photogrammetry, photogrammetry, RANSAC, Collinearity equations, Matching
Abstract. The process of finding correspondence points among the overlapping images is called matching. The matching process is one of the fundamental steps in photogrammetry and computer vision with primarily application in 3D model reconstruction. The main limitation with matching algorithms is finding all the correct matches, so-called inliers, and consequently, reducing the incorrect matches, so-called outliers. A number of algorithms have been developed to increase the inliers. One of the well-known algorithms is RANdom SAmple Consensus (RANSAC). RANSAC, however, has a few limitations in terms of the number of iterations, high false-positive rate (outliers), and computational time. To improve RANSAC we are proposing three enhancements steps. The enhancements utilise an Iterative Least-Squares-based Loop (ILSL), a Similarity Termination (ST) Criterion, and a Post-Processing (PoP) step. We tested our enhancements on unmanned aerial vehicles (UAV) images of a forested area. Results show that the proposed enhancements decrease the false-positive ratio (outliers) and increase the number of inliers, with a reduced computational time compared to the conventional RANSAC. This led to more accurate photogrammetry products including Digital Surface Model (DSM).