A robust and accurate feature matching method for multi-modal geographic images spatial registration

Abstract

While the current research has achieved satisfactory results for the registration of single mode data, there has always been a significant challenge in the registration of multi-modal images due to the obvious nonlinear radiation differences caused by different imaging mechanisms and imaging time. For example, multi-temporal visible, visible-synthetic aperture radar, visible-near infrared, near infrared-short wave infrared, visible-MAP, etc. To address this problem, we propose a Robust and Accurate Feature Matching Method for Multi-modal Geographic Images Spatial Registration (RAMMR) to fully extract common key points between images, weaken the radiation difference between data, and finally accurately match more inliers to realize multi-modal image registration. Considering the influence of noise and edge information on key point extraction, RAMMR first constructs a new scale space by introducing the Side Window Filter (SWF); Then, we improve Harris algorithm to extract key points based on the SWF scale space; After that, we propose an enhanced log-polar descriptor based on the gradient angles and gradient amplitudes of the scale space, which effectively improves the quality of the descriptor and avoids the mismatch of key points; Based on the standard Euclidean distance, we design a re-match strategy to obtain the initial matching results, and Random Sample Consensus (RANSAC) is used to eliminate outliers. Finally, the affine transformation parameters are calculated based on inliers, and multi-modal image registration is realized. RAMMR is evaluated on different multi-modal datasets and compared with some state-of-art methods. The experimental results show that RAMMR accurately registers multi-modal geographic images and obtains comparative results compared with benchmark methods. Our source datasets are publicly available at https://github.com/RSmfmr/multimodal-dataset.

Topics & Concepts