Using Texture to Classify Forests Separately from Vegetation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929479019921408 |
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| author | Treadwell IV, David R. Jacoby, Derek Parkinson, Will Maxwell, Bruce Coady, Yvonne |
| author_facet | Treadwell IV, David R. Jacoby, Derek Parkinson, Will Maxwell, Bruce Coady, Yvonne |
| contents | Identifying terrain within satellite image data is a key issue in geographical information sciences, with numerous environmental and safety implications. Many techniques exist to derive classifications from spectral data captured by satellites. However, the ability to reliably classify vegetation remains a challenge. In particular, no precise methods exist for classifying forest vs. non-forest vegetation in high-level satellite images. This paper provides an initial proposal for a static, algorithmic process to identify forest regions in satellite image data through texture features created from detected edges and the NDVI ratio captured by Sentinel-2 satellite images. With strong initial results, this paper also identifies the next steps to improve the accuracy of the classification and verification processes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_00264 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using Texture to Classify Forests Separately from Vegetation Treadwell IV, David R. Jacoby, Derek Parkinson, Will Maxwell, Bruce Coady, Yvonne Computer Vision and Pattern Recognition Identifying terrain within satellite image data is a key issue in geographical information sciences, with numerous environmental and safety implications. Many techniques exist to derive classifications from spectral data captured by satellites. However, the ability to reliably classify vegetation remains a challenge. In particular, no precise methods exist for classifying forest vs. non-forest vegetation in high-level satellite images. This paper provides an initial proposal for a static, algorithmic process to identify forest regions in satellite image data through texture features created from detected edges and the NDVI ratio captured by Sentinel-2 satellite images. With strong initial results, this paper also identifies the next steps to improve the accuracy of the classification and verification processes. |
| title | Using Texture to Classify Forests Separately from Vegetation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.00264 |