A Novel Dense Image Matching Point Cloud Filtering Algorithm Integrating Visible Light and Progressive Triangulated Irregular Network Densification for High-Accuracy Mining Subsidence Monitoring.
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| Title: | A Novel Dense Image Matching Point Cloud Filtering Algorithm Integrating Visible Light and Progressive Triangulated Irregular Network Densification for High-Accuracy Mining Subsidence Monitoring. |
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| Authors: | Zhang, Mingmei1,2 (AUTHOR), He, Yibo2 (AUTHOR) hyb2025@sxie.edu.cn, Hu, Zhenqi3 (AUTHOR), Wang, Rui4 (AUTHOR), Zhou, Dawei1,3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1408. 32p. |
| Subjects: | Mine subsidences, Point cloud, Digital elevation models, Image registration, Environmental monitoring, Drone aircraft |
| Abstract: | Highlights: What are the main findings? A novel DIM point cloud filtering method named H-PTD has been proposed. This method integrates visible light information to optimize the initial ground seed selection for PTD, overcoming the limitations of only relying on geometric information, and thus enabling the extraction of surface subsidence data of the mining area with high accuracy. By comparing H-PTD with five classical methods using the cross-matrix and DEM difference, the H-PTD method demonstrated higher filtering accuracy and stronger terrain adaptability in different slope mining area terrains. What are the implications of the main findings? By significantly improving the accuracy of extracting surface points in vegetation-covered mining areas, H-PTD provides more reliable technical support for surface subsidence monitoring in mining areas, which is crucial for effective land reclamation and ecological reconstruction. Successfully applying color information to point cloud filtering not only expands the application potential of unmanned aerial vehicle technology in complex mining area environments, but also promotes its in-depth application in environmental monitoring and sustainable development fields. Effective monitoring of surface damage in mining areas is vital for ecological restoration. Unmanned aerial vehicles (UAVs) have been widely used to obtain ground subsidence data owing to their low cost and ease of operation. The images captured by UAVs can generate dense image matching (DIM) point clouds, which, after screening, can be used to create a digital elevation model (DEM) required for deformation analysis. Existing filtering algorithms mainly rely on the spatial geometric features of point clouds and rarely utilize color information, which limits their accuracy in areas with vegetation coverage. To address this issue, this study proposes a H-PTD method that combines visible light with progressive triangulated irregular network densification (PTD). First, initial ground seeds are selected based on the H value in the HSV space. Subsequently, a triangulated irregular network (TIN) is constructed, and iterative densification is performed by evaluating the relationship between the target point and adjacent triangular faces, thereby achieving an accurate distinction between ground and non-ground. Evaluated on three terrain datasets and against five classical methods, the results indicate that the Total error in the H-PTD cross-matrix is controlled between 2.9% and 7.8%, and remains below 8% overall. The standard deviation of the DEM difference is around 0.02 m. Compared to other methods, H-PTD shows higher filtering accuracy and better terrain adaptability, making it more promising for monitoring mining areas and providing a more reliable tool for subsidence detection based on UAVs. [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: 193715439 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Novel Dense Image Matching Point Cloud Filtering Algorithm Integrating Visible Light and Progressive Triangulated Irregular Network Densification for High-Accuracy Mining Subsidence Monitoring. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Mingmei%22">Zhang, Mingmei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Yibo%22">He, Yibo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hyb2025@sxie.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Zhenqi%22">Hu, Zhenqi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Rui%22">Wang, Rui</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Dawei%22">Zhou, Dawei</searchLink><relatesTo>1,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1408. 32p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Mine+subsidences%22">Mine subsidences</searchLink><br /><searchLink fieldCode="DE" term="%22Point+cloud%22">Point cloud</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+elevation+models%22">Digital elevation models</searchLink><br /><searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A novel DIM point cloud filtering method named H-PTD has been proposed. This method integrates visible light information to optimize the initial ground seed selection for PTD, overcoming the limitations of only relying on geometric information, and thus enabling the extraction of surface subsidence data of the mining area with high accuracy. By comparing H-PTD with five classical methods using the cross-matrix and DEM difference, the H-PTD method demonstrated higher filtering accuracy and stronger terrain adaptability in different slope mining area terrains. What are the implications of the main findings? By significantly improving the accuracy of extracting surface points in vegetation-covered mining areas, H-PTD provides more reliable technical support for surface subsidence monitoring in mining areas, which is crucial for effective land reclamation and ecological reconstruction. Successfully applying color information to point cloud filtering not only expands the application potential of unmanned aerial vehicle technology in complex mining area environments, but also promotes its in-depth application in environmental monitoring and sustainable development fields. Effective monitoring of surface damage in mining areas is vital for ecological restoration. Unmanned aerial vehicles (UAVs) have been widely used to obtain ground subsidence data owing to their low cost and ease of operation. The images captured by UAVs can generate dense image matching (DIM) point clouds, which, after screening, can be used to create a digital elevation model (DEM) required for deformation analysis. Existing filtering algorithms mainly rely on the spatial geometric features of point clouds and rarely utilize color information, which limits their accuracy in areas with vegetation coverage. To address this issue, this study proposes a H-PTD method that combines visible light with progressive triangulated irregular network densification (PTD). First, initial ground seeds are selected based on the H value in the HSV space. Subsequently, a triangulated irregular network (TIN) is constructed, and iterative densification is performed by evaluating the relationship between the target point and adjacent triangular faces, thereby achieving an accurate distinction between ground and non-ground. Evaluated on three terrain datasets and against five classical methods, the results indicate that the Total error in the H-PTD cross-matrix is controlled between 2.9% and 7.8%, and remains below 8% overall. The standard deviation of the DEM difference is around 0.02 m. Compared to other methods, H-PTD shows higher filtering accuracy and better terrain adaptability, making it more promising for monitoring mining areas and providing a more reliable tool for subsidence detection based on UAVs. [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/rs18091408 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 1408 Subjects: – SubjectFull: Mine subsidences Type: general – SubjectFull: Point cloud Type: general – SubjectFull: Digital elevation models Type: general – SubjectFull: Image registration Type: general – SubjectFull: Environmental monitoring Type: general – SubjectFull: Drone aircraft Type: general Titles: – TitleFull: A Novel Dense Image Matching Point Cloud Filtering Algorithm Integrating Visible Light and Progressive Triangulated Irregular Network Densification for High-Accuracy Mining Subsidence Monitoring. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Mingmei – PersonEntity: Name: NameFull: He, Yibo – PersonEntity: Name: NameFull: Hu, Zhenqi – PersonEntity: Name: NameFull: Wang, Rui – PersonEntity: Name: NameFull: Zhou, Dawei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 9 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
