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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.20954 |
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| _version_ | 1866914011245707264 |
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| author | Jmal, Amir Chtourou, Chaima Louati, Mahdi Kallel, Abdelaziz Khmila, Houda |
| author_facet | Jmal, Amir Chtourou, Chaima Louati, Mahdi Kallel, Abdelaziz Khmila, Houda |
| contents | In the context of proven climate change, maintaining olive biodiversity through early anomaly detection and treatment using remote sensing technology is crucial, offering effective management solutions. This paper presents an innovative approach to olive tree segmentation from satellite images. By leveraging foundational models and advanced segmentation techniques, the study integrates the Segment Anything Model (SAM) to accurately identify and segment olive trees in agricultural plots. The methodology includes SAM segmentation and corrections based on trees alignement in the field and a learanble constraint about the shape and the size. Our approach achieved a 98\% accuracy rate, significantly surpassing the initial SAM performance of 82\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20954 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Olive Tree Satellite Image Segmentation Based On SAM and Multi-Phase Refinement Jmal, Amir Chtourou, Chaima Louati, Mahdi Kallel, Abdelaziz Khmila, Houda Computer Vision and Pattern Recognition In the context of proven climate change, maintaining olive biodiversity through early anomaly detection and treatment using remote sensing technology is crucial, offering effective management solutions. This paper presents an innovative approach to olive tree segmentation from satellite images. By leveraging foundational models and advanced segmentation techniques, the study integrates the Segment Anything Model (SAM) to accurately identify and segment olive trees in agricultural plots. The methodology includes SAM segmentation and corrections based on trees alignement in the field and a learanble constraint about the shape and the size. Our approach achieved a 98\% accuracy rate, significantly surpassing the initial SAM performance of 82\%. |
| title | Olive Tree Satellite Image Segmentation Based On SAM and Multi-Phase Refinement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.20954 |