The atmosphere distorts the spectrum of remotely sensed data, negatively affecting all forms of investigating Earth's surface. To gather reliable data, it is vital that atmospheric corrections are accurate. The current state of the field of atmospheric correction does not account well for the benefits and costs of different correction algorithms. Ground spectral data are required to evaluate these algorithms better. This dissertation explores using cameras as radiometers as a means of gathering ground spectral data.
I introduce techniques to implement a camera systems for atmospheric correction using off the shelf parts. To aid the design of future camera systems for radiometric correction, methods for estimating the system error prior to construction, calibration and testing of the resulting camera system are explored. Simulations are used to investigate the relationship between the reflectance accuracy of the camera system and the quality of atmospheric correction. In the design phase, read noise and filter choice are found to be the strongest sources of system error. I explain the calibration methods for the camera system, showing the problems of pixel to angle calibration, and adapting the web camera for scientific work. The camera system is tested in the field to estimate its ability to recover directional reflectance from BRF data. I estimate the error in the system due to the experimental set up, then explore how the system error changes with different cameras, environmental set-ups and inversions. With these experiments, I learn about the importance of the dynamic range of the camera, and the input ranges used for the PROSAIL inversion. Evidence that the camera can perform within the specification set for ELM correction in this dissertation is evaluated. The analysis is concluded by simulating an ELM correction of a scene using various numbers of calibration targets, and levels of system error, to find the number of cameras needed for a full-scale implementation.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/624563 |
| Date | January 2017 |
| Creators | Kautz, Jess S., Kautz, Jess S. |
| Contributors | Yool, Stephen R., Yool, Stephen R., Tyo, J Scott, Czapla-Myers, Jeffery |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Electronic Dissertation |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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