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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18050781 |
