Multi-Semantics-Aware Optimal Feature Matching for Multimodal Remote Sensing Images
Yun Liao, Yide Di, Yubing Liu, Hao Zhou, Xuewen Tan, Junhui Liu, Qing Duan · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2026
Feature matching for multi-modal remote sensing images is a critical and challenging task due to the substantial differences in imaging mechanisms across diverse sensors. Traditional feature matching methods often struggle to achieve high accuracy under such conditions. To better exploit the structural and semantic information inherent in multi-modal remote sensing data, we propose MSAMatch, a Multi-Semantics-Aware optimal feature Matching method. MSAMatch incorporates multi-level and multi-semantic attention mechanisms to enhance cross-modal feature extraction and information interaction, thereby significantly improving matching accuracy. Furthermore, we introduce a semantic-guided global matching strategy that integrates se mantic cues with local features to enhance robustness. Finally, an optimal fine-level matching is achieved through bidirectional probabilistic modeling based on Gaussian distributions. Extensive experiments on several benchmark multi-modal remote sensing datasets demonstrate that MSAMatch achieves state-of-the-art performance in feature matching tasks. The code will be available at: https://github.com/LiaoYun0x0/MSAMatch