SAR Image Registration Based on Optimized Ransac Algorithm with Mixed Feature Extraction

Abstract

In this paper, to address the problem in the registration of synthetic aperture radar (SAR), the speeded up robust features (SURF) approach based on multi-level FAST and improved random sampling consistency is proposed. This approach is used to address the high mismatch rate in SAR image registration, and it is based on the characteristics of SAR images. We construct a new corner detection by combining with the hierarchical theory of the system, which is referred to as multi-level FAST (MFAST). The contribution of MFAST algorithm mainly lies in the efficiency of SURF algorithm, which determines the main direction and feature descriptor. And improving the random sampling consistency (RANSAC) to remove mismatched feature points and ameliorate the registration effectiveness. The proposed algorithm is suitable for multi-sensor images with large gray differences and significant edge features. The experimental results show that the registration efficiency of the proposed algorithm is more than three times of the SURF algorithm, and the registration accuracy also is better than traditional SURF algorithm, which can reach 0.39 pixels.