Mathematical Analysis of Image Matching Techniques
This is an incremental study that addresses image matching for applications in robotics, remote sensing, and geospatial data analysis.
The paper tackled the problem of evaluating classical local feature-based image matching algorithms (SIFT and ORB) on satellite imagery, finding that the Inlier Ratio varies with the number of extracted keypoints, though specific numerical results are not provided in the abstract.
Image matching is a fundamental problem in Computer Vision with direct applications in robotics, remote sensing, and geospatial data analysis. We present an analytical and experimental evaluation of classical local feature-based image matching algorithms on satellite imagery, focusing on the Scale-Invariant Feature Transform (SIFT) and the Oriented FAST and Rotated BRIEF (ORB). Each method is evaluated through a common pipeline: keypoint detection, descriptor extraction, descriptor matching, and geometric verification via RANSAC with homography estimation. Matching quality is assessed using the Inlier Ratio - the fraction of correspondences consistent with the estimated homography. The study uses a manually constructed dataset of GPS-annotated satellite image tiles with intentional overlaps. We examine the impact of the number of extracted keypoints on the resulting Inlier Ratio.