Explorable 3D Hyperspectral Models from Multi-Angle Gimballed LWIR Pushbroom Imagery.
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| Title: | Explorable 3D Hyperspectral Models from Multi-Angle Gimballed LWIR Pushbroom Imagery. |
|---|---|
| Authors: | Golosov, Nikolay1,2 (AUTHOR) golosov@psu.edu, Cervone, Guido1,2,3 (AUTHOR), Salvador, Mark3,4 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p781. 27p. |
| Subjects: | Hyperspectral imaging systems, Texture mapping, Materials analysis, Remote sensing, Digital photogrammetry, Spectral imaging |
| Abstract: | Highlights: What are the main findings? Co-registering gimballed pushbroom hyperspectral imagery with RGB frame camera data enables 3D reconstruction using commercial photogrammetric software. A texture-to-image mapping algorithm preserves the link between 3D model coordinates and original hyperspectral pixels, enabling retrieval of multi-angle spectra (8–50 viewing angles) for any point on the reconstructed model. What is the implication of the main finding? Explorable 3D hyperspectral models allow for interactive analysis of how long-wave infrared spectral signatures vary with viewing angle, supporting material identification for non-Lambertian surfaces where single-angle observations may be insufficient. The workflow bridges the gap between specialized hyperspectral sensors and widely available photogrammetry tools, making multi-angle LWIR remote sensing more accessible for applications such as chemical detection, geological mapping, and environmental monitoring. Hyperspectral imaging in the long-wave infrared (LWIR) range enables identification of chemical compositions and material properties, but reconstructing 3D models from gimballed pushbroom sensors remains challenging because their unique acquisition geometry is incompatible with conventional photogrammetric software designed for frame cameras. This study presents a workflow for creating explorable 3D models from multi-angle LWIR hyperspectral imagery by co-registering hyperspectral line-scan data with simultaneously acquired RGB frame camera imagery using deep learning-based image matching. The co-registered images are processed in commercial photogrammetric software (Agisoft Metashape), and a texture-to-image mapping algorithm preserves correspondences between 3D model coordinates and original hyperspectral pixels across multiple viewing angles. Quantitative evaluation against reference data demonstrates that co-registration reduces geometric error approaching the accuracy of models built from high-resolution RGB imagery. The resulting models enable the retrieval of 8–50 spectral signatures per surface point, captured from different viewing geometries. This approach facilitates interactive exploration of angular variations in thermal infrared spectra, supporting material identification for non-Lambertian surfaces where single-angle observations may be insufficient for reliable classification. [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: 192640038 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Explorable 3D Hyperspectral Models from Multi-Angle Gimballed LWIR Pushbroom Imagery. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Golosov%2C+Nikolay%22">Golosov, Nikolay</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> golosov@psu.edu</i><br /><searchLink fieldCode="AR" term="%22Cervone%2C+Guido%22">Cervone, Guido</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Salvador%2C+Mark%22">Salvador, Mark</searchLink><relatesTo>3,4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Mar2026, Vol. 18 Issue 5, p781. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hyperspectral+imaging+systems%22">Hyperspectral imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Texture+mapping%22">Texture mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Materials+analysis%22">Materials analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+photogrammetry%22">Digital photogrammetry</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+imaging%22">Spectral imaging</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Co-registering gimballed pushbroom hyperspectral imagery with RGB frame camera data enables 3D reconstruction using commercial photogrammetric software. A texture-to-image mapping algorithm preserves the link between 3D model coordinates and original hyperspectral pixels, enabling retrieval of multi-angle spectra (8–50 viewing angles) for any point on the reconstructed model. What is the implication of the main finding? Explorable 3D hyperspectral models allow for interactive analysis of how long-wave infrared spectral signatures vary with viewing angle, supporting material identification for non-Lambertian surfaces where single-angle observations may be insufficient. The workflow bridges the gap between specialized hyperspectral sensors and widely available photogrammetry tools, making multi-angle LWIR remote sensing more accessible for applications such as chemical detection, geological mapping, and environmental monitoring. Hyperspectral imaging in the long-wave infrared (LWIR) range enables identification of chemical compositions and material properties, but reconstructing 3D models from gimballed pushbroom sensors remains challenging because their unique acquisition geometry is incompatible with conventional photogrammetric software designed for frame cameras. This study presents a workflow for creating explorable 3D models from multi-angle LWIR hyperspectral imagery by co-registering hyperspectral line-scan data with simultaneously acquired RGB frame camera imagery using deep learning-based image matching. The co-registered images are processed in commercial photogrammetric software (Agisoft Metashape), and a texture-to-image mapping algorithm preserves correspondences between 3D model coordinates and original hyperspectral pixels across multiple viewing angles. Quantitative evaluation against reference data demonstrates that co-registration reduces geometric error approaching the accuracy of models built from high-resolution RGB imagery. The resulting models enable the retrieval of 8–50 spectral signatures per surface point, captured from different viewing geometries. This approach facilitates interactive exploration of angular variations in thermal infrared spectra, supporting material identification for non-Lambertian surfaces where single-angle observations may be insufficient for reliable classification. [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/rs18050781 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 781 Subjects: – SubjectFull: Hyperspectral imaging systems Type: general – SubjectFull: Texture mapping Type: general – SubjectFull: Materials analysis Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Digital photogrammetry Type: general – SubjectFull: Spectral imaging Type: general Titles: – TitleFull: Explorable 3D Hyperspectral Models from Multi-Angle Gimballed LWIR Pushbroom Imagery. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Golosov, Nikolay – PersonEntity: Name: NameFull: Cervone, Guido – PersonEntity: Name: NameFull: Salvador, Mark IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 5 Titles: – TitleFull: Remote Sensing Type: main |
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