fabSAM：基于Segment Anything Model的农田边界划定方法
fabSAM: A Farmland Boundary Delineation Method Based on the Segment Anything Model
AI总结：
提出基于SAM和Deeplabv3+提示器的fabSAM框架，通过微调策略提升农田边界划定精度，在AI4Boundaries和AI4SmallFarms数据集上mIOU较零样本SAM提升23.5%和15.1%。
摘要：
划定农田边界对于作物监测和农业普查等农业管理至关重要。传统方法利用遥感影像虽高效，但泛化能力有限。Segment Anything Model（SAM）以其出色的零样本性能而闻名，已通过提示学习和微调被应用于遥感任务。在此，我们提出一个基于SAM的农田边界划定框架“fabSAM”，该框架结合了基于Deeplabv3+的提示器（Prompter）和SAM。同时，引入了一种微调策略，使SAM的解码器能够更好地利用提示信息。在AI4Boundaries和AI4SmallFarms数据集上的实验结果表明，fabSAM在农田区域识别和边界划定方面有显著提升。与零样本SAM相比，fabSAM在AI4Boundaries和AI4SmallFarms数据集上的mIOU分别提高了23.5%和15.1%。与Deeplabv3+相比，fabSAM的mIOU分别提高了4.9%和12.5%。这些结果凸显了fabSAM的有效性，也意味着我们可以更轻松地从Sentinel2等开源卫星影像数据集中获取全球农田区域和边界图。
英文摘要：
Delineating farmland boundaries is essential for agricultural management such as crop monitoring and agricultural census. Traditional methods using remote sensing imagery have been efficient but limited in generalisation. The Segment Anything Model (SAM), known for its impressive zero shot performance, has been adapted for remote sensing tasks through prompt learning and fine tuning. Here, we propose a SAM based farmland boundary delineation framework 'fabSAM' that combines a Deeplabv3+ based Prompter and SAM. Also, a fine tuning strategy was introduced to enable SAMs decoder to improve the use of prompt information. Experimental results on the AI4Boundaries and AI4SmallFarms datasets have shown that fabSAM has a significant improvement in farmland region identification and boundary delineation. Compared to zero shot SAM, fabSAM surpassed it by 23.5% and 15.1% in mIOU on the AI4Boundaries and AI4SmallFarms datasets, respectively. For Deeplabv3+, fabSAM outperformed it by 4.9% and 12.5% in mIOU, respectively. These results highlight the effectiveness of fabSAM, which also means that we can more easily obtain the global farmland region and boundary maps from open source satellite image datasets like Sentinel2.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
