Improving Hyperspectral Image Consistency for Vegetation Classification over the Port Hedland Coastal Region
The brightness consistency of hyperspectral airborne data over the Port Hedland coastal region was improved through a newly developed mathematical-based technique termed normalisation. Classification of the normalised data resulted in improved spatially coherent vegetation structures, with particula...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2020
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| Online Access: | http://hdl.handle.net/20.500.11937/82565 |
| _version_ | 1848764521001254912 |
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| author | Bos, Shawn Henson |
| author_facet | Bos, Shawn Henson |
| author_sort | Bos, Shawn Henson |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The brightness consistency of hyperspectral airborne data over the Port Hedland coastal region was improved through a newly developed mathematical-based technique termed normalisation. Classification of the normalised data resulted in improved spatially coherent vegetation structures, with particular emphasis on mangroves. In addition, spatial statistical analysis ensured the structures were well-defined to a suitable probability, which compared favourably to the results of an earlier commercial survey based on photointerpretation and field work. |
| first_indexed | 2025-11-14T11:20:40Z |
| format | Thesis |
| id | curtin-20.500.11937-82565 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:20:40Z |
| publishDate | 2020 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-825652023-01-30T00:58:40Z Improving Hyperspectral Image Consistency for Vegetation Classification over the Port Hedland Coastal Region Bos, Shawn Henson The brightness consistency of hyperspectral airborne data over the Port Hedland coastal region was improved through a newly developed mathematical-based technique termed normalisation. Classification of the normalised data resulted in improved spatially coherent vegetation structures, with particular emphasis on mangroves. In addition, spatial statistical analysis ensured the structures were well-defined to a suitable probability, which compared favourably to the results of an earlier commercial survey based on photointerpretation and field work. 2020 Thesis http://hdl.handle.net/20.500.11937/82565 Curtin University fulltext |
| spellingShingle | Bos, Shawn Henson Improving Hyperspectral Image Consistency for Vegetation Classification over the Port Hedland Coastal Region |
| title | Improving Hyperspectral Image Consistency for Vegetation Classification over the Port Hedland Coastal Region |
| title_full | Improving Hyperspectral Image Consistency for Vegetation Classification over the Port Hedland Coastal Region |
| title_fullStr | Improving Hyperspectral Image Consistency for Vegetation Classification over the Port Hedland Coastal Region |
| title_full_unstemmed | Improving Hyperspectral Image Consistency for Vegetation Classification over the Port Hedland Coastal Region |
| title_short | Improving Hyperspectral Image Consistency for Vegetation Classification over the Port Hedland Coastal Region |
| title_sort | improving hyperspectral image consistency for vegetation classification over the port hedland coastal region |
| url | http://hdl.handle.net/20.500.11937/82565 |