Two-Step Matching Method Based on Co-Occurrence Scale Space Combined with Second-Order Gaussian Steerable Filter

Abstract

Multimodal images refer to images obtained by different sensors, and there are serious nonlinear radiation differences (NRDs) between multimodal images for photos of the same object. Traditional multimodal image matching methods cannot achieve satisfactory results in most cases. In order to better solve the NRD in multimodal image matching, as well as the rotation and scale problems, we propose a two-step matching method based on co-occurrence scale space combined with the second-order Gaussian steerable filter (G-CoFTM). We first use the second-order Gaussian steerable filter and co-occurrence filter to construct the image’s scale space to preserve the image’s edge and detail features. Secondly, we use the second-order gradient direction to calculate the images’ principal direction, and describe the images’ feature points through improved GLOH descriptors. Finally, after obtaining the rough matching results, the optimized 3DPC descriptors are used for template matching to complete the fine matching of the images. We validate our proposed G-CoFTM method on eight different types of multimodal datasets and compare it with five state-of-the-art methods: PSO-SIFT, CoFSM, RIFT, HAPCG, and LPSO. Experimental results show that our proposed method has obvious advantages in matching success rate (SR) and the number of correct matches (NCM). On eight different types of datasets, compared with CoFSM, RIFT, HAPCG, and LPSO, the mean SRs of G-CoFSM are 17.5%, 6.187%, 30.462%, and 32.21%, respectively, and the mean NCMs are 5.322, 11.503, 8.607, and 16.429 times those of the above four methods.