# A Mutual-Structure Weighted Sub-Pixel Multimodal Optical Remote Sensing Image Matching Method

arXiv: https://arxiv.org/abs/2508.10294 · HTML: https://mlsanity.com/paper/2508.10294/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 64 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model](https://mlsanity.com/paper/1709.03966/) (2017)
- [Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity](https://mlsanity.com/paper/2103.16871/) (2021)

## Abstract (arXiv)

Sub-pixel matching of multimodal optical images is a critical step in combined application of multiple sensors. However structural noise and inconsistencies arising from variations in multimodal image responses usually limit the accuracy of matching. Phase congruency mutual-structure weighted least absolute deviation (PCWLAD) is developed as a coarse-to-fine framework. In the coarse matching stage, we preserve the complete structure and use an enhanced cross-modal similarity criterion to mitigate structural information loss by PC noise filtering. In the fine matching stage, a mutual-structure filtering and weighted least absolute deviation-based is introduced to enhance inter-modal structural consistency and accurately estimate sub-pixel displacements adaptively. Experiments on three multimodal datasets-Landsat visible-infrared, short-range visible-near-infrared, and UAV optical image pairs demonstrate that PCWLAD consistently outperforms eight state-of-the-art methods, achieving an average matching accuracy of approximately 0.4 pixels. The software and datasets are publicly available at https://github.com/huangtaocsu/PCWLAD.