Sentinel-1 SAR Based Weakly Supervised Learning For Tropical Forest Mapping
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914897228464128 |
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| author | Mullissa, Adugna Saatchi, Sassan |
| author_facet | Mullissa, Adugna Saatchi, Sassan |
| contents | Tropical forests play an important role in regulating the global carbon cycle and are crucial for maintaining the tropical forest biodiversity. Therefore, there is an urgent need to map the extent of tropical forest ecosystems. Recently, deep learning has come out as a powerful tool to map these ecosystems with the caveat of curating high quality reference datasets. Since, manually annotating high quality reference datasets is time consuming and expensive, weakly supervised learning techniques offer the potential to train high quality models without the need for manually annotating large quantities of reference datasets. In this manuscript, we propose two weakly supervised approaches that are based on Sentinel-1 SAR images, sparsely distributed pixel-wise high quality reference labels and densely distributed noisy reference labels. The proposed approaches were tested in a tropical setting in the Brazilian amazon. The results demonstrate that high quality tropical forest maps can be derived from weakly supervised learning without the need for manually annotated labels. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_00107 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sentinel-1 SAR Based Weakly Supervised Learning For Tropical Forest Mapping Mullissa, Adugna Saatchi, Sassan Image and Video Processing Tropical forests play an important role in regulating the global carbon cycle and are crucial for maintaining the tropical forest biodiversity. Therefore, there is an urgent need to map the extent of tropical forest ecosystems. Recently, deep learning has come out as a powerful tool to map these ecosystems with the caveat of curating high quality reference datasets. Since, manually annotating high quality reference datasets is time consuming and expensive, weakly supervised learning techniques offer the potential to train high quality models without the need for manually annotating large quantities of reference datasets. In this manuscript, we propose two weakly supervised approaches that are based on Sentinel-1 SAR images, sparsely distributed pixel-wise high quality reference labels and densely distributed noisy reference labels. The proposed approaches were tested in a tropical setting in the Brazilian amazon. The results demonstrate that high quality tropical forest maps can be derived from weakly supervised learning without the need for manually annotated labels. |
| title | Sentinel-1 SAR Based Weakly Supervised Learning For Tropical Forest Mapping |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2408.00107 |