Multimodal Remote Sensing Image Matching Based on Weighted Structure Saliency Feature

Abstract

Matching multimodal remote sensing images (MRSIs) is a challenging task. Due to significant nonlinear radiation differences (NRDs), traditional image-matching methods cannot achieve satisfactory results. This article shows that structural information can get more robust matching results compared with texture information (i.e., gradient features) from images. In order to better explore the structural information of images, this article proposes an MRSI matching method using structure saliency features, called weighted structure saliency feature (WSSF). Two strategies are investigated and integrated into WSSF to improve the matching performance. The scale space is constructed based on the pointwise shape-adaptive texture scale filtering, which can better retain the structure features, and the second-order Gaussian steerable filtering, edge confidence map, and phase features are combined to establish the structural saliency map combined with second-order Gaussian steerable filtering, which is much more robust to NRD than traditional gradient map. The performance of the proposed method was evaluated on a total of 120 image pairs from two MRSI datasets and compared with the state-of-the-art matching methods, including the histogram of the orientation of weighted phase (HOWP), locally normalized image feature transform (LNIFT), co-occurrence filter space matching (CoFSM), radiation-variation insensitive feature transform (RIFT), local phase sharpness orientation (LPSO), and position-scale-orientation scale-invariant feature transform (SIFT) (PSO-SIFT). The experimental results indicate that WSSF obtains satisfactory and reliable results in terms of success rate (SR) and matching accuracy. Compared with the above six methods, the matching accuracy of WSSF is improved by more than 20.275%, and the SR is improved by over 5.833%. The source code will be publicly available at https://github.com/WGY-RS/ WSSF.