Robust Feature Matching with Spatial Smoothness Constraints

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 19; p. 3158
• Main Authors: Huang, Xu, Wan, Xue, Peng, Daifeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2020
• Subjects: Accuracy; Algorithms; Cameras; Computer applications; data collection; Delaunay triangulation; Energy; feature matching; global energy function; Global optimization; Limiting factors; Matching; Methods; Optimization; Photogrammetry; remote sensing; Robustness; SIFT; Smoothness; spatial smoothness constraint; SURF
• DOI: 10.3390/rs12193158
• ISSN: 2072-4292

Feature matching is to detect and match corresponding feature points in stereo pairs, which is one of the key techniques in accurate camera orientations. However, several factors limit the feature matching accuracy, e.g., image textures, viewing angles of stereo cameras, and resolutions of stereo pairs. To improve the feature matching accuracy against these limiting factors, this paper imposes spatial smoothness constraints over the whole feature point sets with the underlying assumption that feature points should have similar matching results with their surrounding high-confidence points and proposes a robust feature matching method with the spatial smoothness constraints (RMSS). The core algorithm constructs a graph structure from the feature point sets and then formulates the feature matching problem as the optimization of a global energy function with first-order, spatial smoothness constraints based on the graph. For computational purposes, the global optimization of the energy function is then broken into sub-optimizations of each feature point, and an approximate solution of the energy function is iteratively derived as the matching results of the whole feature point sets. Experiments on close-range datasets with some above limiting factors show that the proposed method was capable of greatly improving the matching robustness and matching accuracy of some feature descriptors (e.g., scale-invariant feature transform (SIFT) and Speeded Up Robust Features (SURF)). After the optimization of the proposed method, the inlier number of SIFT and SURF was increased by average 131.9% and 113.5%, the inlier percentages between the inlier number and the total matches number of SIFT and SURF were increased by average 259.0% and 307.2%, and the absolute matching accuracy of SIFT and SURF was improved by average 80.6% and 70.2%.