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] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193435616 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Mutual-Structure Weighted Sub-Pixel Multimodal Optical Remote Sensing Image Matching Method. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huang%2C+Tao%22">Huang, Tao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Hongbo%22">Pan, Hongbo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hongbopan@csu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Nanxi%22">Zhou, Nanxi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zou%2C+Siyuan%22">Zou, Siyuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Shun%22">Zhou, Shun</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Apr2026, Vol. 18 Issue 8, p1137. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Multispectral+imaging%22">Multispectral imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+denoising%22">Signal denoising</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18081137 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1137 Subjects: – SubjectFull: Image registration Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Multispectral imaging Type: general – SubjectFull: Signal denoising Type: general Titles: – TitleFull: A Mutual-Structure Weighted Sub-Pixel Multimodal Optical Remote Sensing Image Matching Method. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huang, Tao – PersonEntity: Name: NameFull: Pan, Hongbo – PersonEntity: Name: NameFull: Zhou, Nanxi – PersonEntity: Name: NameFull: Zou, Siyuan – PersonEntity: Name: NameFull: Zhou, Shun IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 8 Titles: – TitleFull: Remote Sensing Type: main |
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