Application of Improved RANSAC Algorithm to Multi-Spectral Image Matching

Abstract

In order to improve the speed and accuracy of multi-spectral image matching, an improved random sampling consistency (RANSAC) algorithm is proposed. For the traditional RANSAC algorithm, it presents such problems as lots of iterations, low operation efficiency and low precision of the homography matrices estimation. In this paper, based on the SIFT algorithm to complete the initial feature matching, the RANSAC algorithm is improved by reasonably reducing the number of elements in a sample set, which can increase the proportion of intra-office points in the sample, and rapidly rejecting unreasonable initial parameter models by using pre-test. This can greatly reduce the number of iterations of the algorithm and improve the operation efficiency and accuracy of the algorithm. Experimental results show that the proposed method not only improves the accuracy of image matching, but also on the premise of the same data processing, it decreases the process time to be less than 60% of that of the RANSAC algorithm, which improves the efficiency of the algorithm.