用SAM模型精准识别农田边界，提升农业管理效率。
fabSAM: A Farmland Boundary Delineation Method Based on the Segment Anything Model
- 结合DeepLabv3+提示器与SAM，实现农田边界自动分割。
- 在两个数据集上比零样本SAM提升23.5%和15.1%的mIOU。
- 适合遥感图像分析、智慧农业及开放卫星数据应用。
农田边界识别对作物监测与农业普查等农业管理至关重要。传统遥感方法虽高效但泛化能力有限。本文提出基于Segment Anything Model（SAM）的农田边界分割框架fabSAM，融合DeepLabv3+提示器与SAM，并引入微调策略优化解码器对提示信息的利用。在AI4Boundaries与AI4SmallFarms数据集上的实验表明，fabSAM在农田区域识别与边界勾画上显著优于基线模型。相比零样本SAM，fabSAM在两个数据集上分别提升23.5%与15.1%的mIOU；相较DeepLabv3+，分别提升4.9%与12.5%。结果验证了fabSAM的有效性，使从Sentinel-2等开源卫星影像中快速获取全球农田范围与边界图成为可能。
原文摘要 · Abstract (English)
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.
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