Multi-Hypothesis Topological Isomorphism Matching Method for Synthetic Aperture Radar Images with Large Geometric Distortion

Runzhi Jiao,Qingsong Wang,Haifeng Huang,Tao Lai

doi:10.3390/rs13224637

Abstract

The dramatic undulations of a mountainous terrain will introduce large geometric distortions in each Synthetic Aperture Radar (SAR) image with different look angles, resulting in a poor registration performance. To this end, this paper proposes a multi-hypothesis topological isomorphism matching method for SAR images with large geometric distortions. The method includes the Ridge-Line Keypoint Detection (RLKD) and Multi-Hypothesis Topological Isomorphism Matching (MHTIM). Firstly, based on the analysis of the ridge structure, a ridge keypoint detection module and a keypoint similarity description method are designed, which aim to quickly produce a small number of stable matching keypoint pairs under large look angle differences and large terrain undulations. The keypoint pairs are further fed into the MHTIM module. Subsequently, the MHTIM method is proposed, which uses the stability and isomorphism of the topological structure of the keypoint set under different perspectives to generate a variety of matching hypotheses, and iteratively achieves the keypoint matching. This method uses both local and global geometric relationships between two keypoints, hence it achieving better performance compared with traditional methods. We tested our approach on both simulated and real mountain SAR images with different look angles and different elevation ranges. The experimental results demonstrate the effectiveness and stable matching performance of our approach.

Highlights

Published: 17 November 2021About 24% of the earth’s land is covered by mountains [1]
For solving the problem of large geometric distortion of Synthetic Aperture Radar (SAR) image matching in mountainous areas caused by large differences in look angles and severe terrain fluctuations, we propose a large geometric distortion SAR image multi-hypothesis topological isomorphism matching method
Vim,vsj vim Γ − vsj where, Γ is the transfer model, and |C| is the number of keypoint pairs that are correctly matched, that is, Number of Keypoints Matched (NKM)

Summary

Introduction

About 24% of the earth’s land is covered by mountains [1]. Since NASA launched its first SAR satellite SEASAT in 1978, several countries have successively deployed multiple spaceborne SAR systems, accumulating massive amounts of SAR image data of mountain areas. In order to jointly exploit these data for elevation inversion, deformation detection, and biomass monitoring, an accurate matching performance becomes a prerequisite. The difference in the viewing angles causes a relative geometric distortion between two images. The larger the difference in the angles, the larger the geometrical deformations. This poses great challenges to the registration of SAR images with large geometric distortion

Results

Discussion

Conclusion