Automated lithological mapping using airborne hyperspectral thermal infrared data : Anchorage Island, Antarctica
The thermal infrared portion of the electromagnetic spectrum has considerable potential for mineral and lithological mapping of the most abundantrock-forming silicates that do not display diagnostic features at visible and shortwave infrared wavelengths. Lithological mapping using visible and shortw...
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| Other Authors: | , |
| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Online Access: | https://hull-repository.worktribe.com/file/4218615/1/Thesis https://hull-repository.worktribe.com/output/4218615 |
| Summary: | The thermal infrared portion of the electromagnetic spectrum has considerable potential for mineral and lithological mapping of the most abundantrock-forming silicates that do not display diagnostic features at visible and shortwave infrared wavelengths. Lithological mapping using visible and shortwave infrared hyperspectral data is well developed and established processing chains are available; however, there is a paucity of such methodologies for hyperspectral thermal infrared data. Here, a new fully automated processing chain for deriving lithological maps from hyperspectral thermal infrared data is presented; the processing chain is developed through testing of existing algorithms on synthetic hyperspectral data. The processing chain is then applied to the first ever airborne hyperspectral thermal data collected in the Antarctic. A combined airborne hyperspectral survey, targeted geological field mapping campaign and detailed mineralogical and geochemical datasets are applied to a small test site in West Antarctica where the geological relationships are typical of continental margin arcs. The challenging environmental conditions and cold temperatures in the Antarctic meant that the data have a significantly lower signal to noise ratio than is usually attained from airborne hyperspectral sensors. Preprocessing techniques were applied to improve the signal to noise ratio and convert the radiance images to ground leaving emissivity. Following preprocessing, the fully automated processing chain was applied to the hyperspectral imagery to generate a lithological map. The results show that the image processing chain was successful, despite the low signal to noise ratio of the imagery; the results are encouraging with the thermal imagery allowing clear distinction between granitoid types. |
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