Active and Passive Sensor Fusion for Terrestrial Hyperspectral Image Shadow Detection and Restoration
| dc.contributor.advisor | Glennie, Craig L. | |
| dc.contributor.committeeMember | Shrestha, Ramesh L. | |
| dc.contributor.committeeMember | Lee, Hyongki | |
| dc.contributor.committeeMember | Khan, Shuhab D. | |
| dc.contributor.committeeMember | Parrish, Christopher E. | |
| dc.creator | Hartzell, Preston J. | |
| dc.creator.orcid | 0000-0002-8293-3706 | |
| dc.date.accessioned | 2018-07-10T18:46:20Z | |
| dc.date.available | 2018-07-10T18:46:20Z | |
| dc.date.created | May 2016 | |
| dc.date.issued | 2016-05 | |
| dc.date.submitted | May 2016 | |
| dc.date.updated | 2018-07-10T18:46:20Z | |
| dc.description.abstract | Acquisition of hyperspectral imagery (HSI) from cameras mounted on terrestrial platforms is a relatively recent development that enables spectral analysis of dominantly vertical structures such as geologic outcrops. Although solar shadowing is prevalent in terrestrial HSI due to the vertical scene geometry, automated shadow detection and restoration algorithms have not yet been applied to this technique. This dissertation investigates the fusion of terrestrial laser scanning (TLS) spatial information with terrestrial HSI for geometric shadow detection on a vertical outcrop and examines the contribution of radiometrically calibrated TLS intensity, which is resistant to the influence of solar shadowing, to HSI shadow restoration. The proposed method for shadow detection in the terrestrial HSI leverages an accurately georeferenced, high density point cloud acquired with a TLS sensor to geometrically solve for the presence of shadows in the fused HSI. In contrast to traditional methods applied to airborne imagery, the analysis requires a fully 3D mesh representation of the outcrop rather than a 2.5D surface model. The inclusion of radiometrically calibrated TLS intensity in several existing image shadow restoration techniques is examined, and a direct combination of the active TLS and passive HSI radiometric products proposed and evaluated. Qualitative assessment of the shadow detection results indicates pixel level accuracy, which is indirectly validated by shadow restoration improvements when sub-pixel shadow detection is used in lieu of single pixel detection. The inclusion of TLS intensity in existing shadow restoration algorithms was found to have a marginal positive influence on restoring shadow spectrum shape, while the proposed combination of TLS intensity with passive HSI spectra boosts restored shadow spectrum magnitude precision by up to 40%, and band correlation with respect to a truth image by up to 45% compared to existing methods. The findings demonstrate that sub-pixel shadow detection in terrestrial HSI can be achieved with geometric methods using standard TLS and HSI field collection practices, and the inclusion of TLS intensity can improve restored HSI spectral characteristics. Simulations incorporating multiple laser wavelengths suggest more robust and computationally efficient methods of combining active and passive spectral data for restoring shadow pixel spectra are possible. | |
| dc.description.department | Civil and Environmental Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10657/3170 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | LiDAR | |
| dc.subject | Laser scanning | |
| dc.subject | Terrestrial laser scanning (TLS) | |
| dc.subject | Hyperspectral imaging | |
| dc.subject | Sensor fusion | |
| dc.subject | Remote sensing | |
| dc.title | Active and Passive Sensor Fusion for Terrestrial Hyperspectral Image Shadow Detection and Restoration | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Civil and Environmental Engineering, Department of | |
| thesis.degree.discipline | Geosensing Systems | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |