Integration of optimal spatial distributed tie-points in RANSAC-based image registration

• Main Authors: Sheng Zhang, Shanshan Li, Bing Zhang, Man Peng
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2020-01-01
• Series: European Journal of Remote Sensing
• Subjects: image registration; spatial distribution; sift; random sample consensus; adaptive stratified partition; stratified random selection
• Online Access: http://dx.doi.org/10.1080/22797254.2020.1724519

Feature-based image registration requires the identification of correct tie-points between the image pair. In this paper, an improved outlier method is proposed to find correct matching results of optimal distribution based on RANSAC (RANdom SAmple Consensus) algorithm. The main feature of the proposed method is that an optimal spatial designation of tie-points method using stratified random selection (SRS), is integrated into RANSAC framework to filter out the mismatched features that exist in the massive initial matches generated by SIFT operator in order to estimate mapping function accurately. In this way, the selection of relatively disperse and evenly distributed tie-points based on adaptive stratified partition can make RANSAC efficient. We carried out experiments on the registration of three pairs of satellite images. The proposed SIFT-SRS-RANSAC method leads to higher matching and registration accuracy when comparing with the performance of SIFT-RANSAC and SIFT-bucketing-RANSAC algorithms.