A Sub-Pixel Multimodal Optical Remote Sensing Images Matching Method

• URL: http://arxiv.org/abs/2508.10294v1
• Date: Thu, 14 Aug 2025 02:49:19 GMT
• Title: A Sub-Pixel Multimodal Optical Remote Sensing Images Matching Method
• Authors: Tao Huang, Hongbo Pan, Nanxi Zhou, Shun Zhou,
• Abstract summary: We propose a phase consistency weighted least absolute deviation (PCWLAD) sub-pixel template matching method to improve the matching accuracy of multimodal optical images.<n>To evaluate the performance of PCWLAD, we created three types of image datasets: visible to infrared Landsat images, visible to near-infrared close-range images, and visible to infrared uncrewed aerial vehicle (UAV) images.
• Score: 2.9300891362823425
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: High-accuracy matching of multimodal optical images is the basis of geometric processing. However, the image matching accuracy is usually degraded by the nonlinear radiation and geometric deformation differences caused by different spectral responses. To address these problems, we proposed a phase consistency weighted least absolute deviation (PCWLAD) sub-pixel template matching method to improve the matching accuracy of multimodal optical images. This method consists of two main steps: coarse matching with the structural similarity index measure (SSIM) and fine matching with WLAD. In the coarse matching step, PCs are calculated without a noise filter to preserve the original structural details, and template matching is performed using the SSIM. In the fine matching step, we applied the radiometric and geometric transformation models between two multimodal PC templates based on the coarse matching. Furthermore, mutual structure filtering is adopted in the model to mitigate the impact of noise within the corresponding templates on the structural consistency, and the WLAD criterion is used to estimate the sub-pixel offset. To evaluate the performance of PCWLAD, we created three types of image datasets: visible to infrared Landsat images, visible to near-infrared close-range images, and visible to infrared uncrewed aerial vehicle (UAV) images. PCWLAD outperformed existing state-of-the-art eight methods in terms of correct matching rate (CMR) and root mean square error (RMSE) and reached an average matching accuracy of approximately 0.4 pixels across all three datasets. Our software and datasets are publicly available at https://github.com/huangtaocsu/PCWLAD.

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