A Mutual-Structure Weighted Sub-Pixel Multimodal Optical Remote Sensing Image Matching Method.
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| Title: | A Mutual-Structure Weighted Sub-Pixel Multimodal Optical Remote Sensing Image Matching Method. |
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| Authors: | Huang, Tao1 (AUTHOR), Pan, Hongbo1 (AUTHOR) hongbopan@csu.edu.cn, Zhou, Nanxi1 (AUTHOR), Zou, Siyuan1 (AUTHOR), Zhou, Shun1 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 8, p1137. 23p. |
| Subjects: | Image registration, Remote sensing, Multispectral imaging, Signal denoising |
| Abstract: | Highlights: What are the main findings? Noise filtering for PCs of different modes has a significant impact on structural similarity. Eliminating structural inconsistencies and noise in images from different modalities is crucial for sub-pixel matching accuracy. What are the implications of the main findings? Achieving sub-pixel matching and high-precision registration of multimodal optical images. This provides a high-precision geometric basis for subsequent multi-sensor remote sensing combined applications. Sub-pixel matching of multimodal optical images is a critical step in the 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 phase congruency (PC) noise filtering. In the fine matching stage, a mutual-structure filtering and weighted least absolute deviation-based method is introduced to enhance inter-modal structural consistency and to accurately estimate sub-pixel displacements adaptively. Experiments on three multimodal datasets—Landsat visible-infrared, short-range visible-near-infrared, and unmanned aerial vehicle (UAV) optical image pairs—show that PCWLAD achieves superior average performance compared with eight state-of-the-art methods, attaining an average matching accuracy of approximately 0.4 pixels. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Noise filtering for PCs of different modes has a significant impact on structural similarity. Eliminating structural inconsistencies and noise in images from different modalities is crucial for sub-pixel matching accuracy. What are the implications of the main findings? Achieving sub-pixel matching and high-precision registration of multimodal optical images. This provides a high-precision geometric basis for subsequent multi-sensor remote sensing combined applications. Sub-pixel matching of multimodal optical images is a critical step in the 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 phase congruency (PC) noise filtering. In the fine matching stage, a mutual-structure filtering and weighted least absolute deviation-based method is introduced to enhance inter-modal structural consistency and to accurately estimate sub-pixel displacements adaptively. Experiments on three multimodal datasets—Landsat visible-infrared, short-range visible-near-infrared, and unmanned aerial vehicle (UAV) optical image pairs—show that PCWLAD achieves superior average performance compared with eight state-of-the-art methods, attaining an average matching accuracy of approximately 0.4 pixels. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18081137 |
