Olive Tree Satellite Image Segmentation Based On SAM and Multi-Phase Refinement
This work addresses early anomaly detection for olive biodiversity management in agriculture, but it is incremental as it builds on existing foundational models with specific refinements.
The paper tackled the problem of segmenting olive trees from satellite images by integrating the Segment Anything Model (SAM) with multi-phase refinement techniques, achieving a 98% accuracy rate which significantly improved upon the initial SAM performance of 82%.
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\%.