使用分段任何模型 (SAM) 进行季节性哨兵2图像的场边界划分
Thuan Ha1, Kwabena Abrefa Nketia1, Hansanee Fernando1
1Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada.
MethodsX
|February 2, 2026
概括
使用Segment Anything Model (SAM) 和Sentinel-2图像的自动化场边界映射实现了0.86 IoU的准确性. 这种可扩展的工作流支持精准农业,通过高效,大规模的作物田区划分.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 准确的田间边界划定对于精准农业 (PA) 和作物产量建模至关重要.
- 传统的方法是劳动密集型的,难以扩展到大型农业面积.
- 高分辨率的卫星图像和先进的模型为自动化解决方案提供了潜力.
研究的目的:
- 开发和验证一个完全自动化的工作流程,用于大规模的现场边界提取.
- 整合分段任何模型 (SAM) 与时间序列的哨兵-2图像,以增强分段.
- 为PA应用提供可复制和适应的方法.
主要方法:
- 使用预训练的基础模型,SAM,与Sentinel-2时间序列图像相结合.
- 在各种现象学阶段生成红色,绿色和蓝色带的季节性复合物.
- 开发了一个四步工作流程:环境设置,谷歌地球引擎预处理,SAM细分和ArcGIS Pro后处理.
主要成果:
- 将工作流应用于加拿大大草原的3200多万公.
- 与手动细分相比,实现了0.86的高交叉对联 (IoU) 精度.
- 展示了一个可扩展和高效的自动化场域边界映射解决方案.
结论:
- 拟议的自动化工作流程有效地在大规模上划定了领域边界.
- 这种方法为精密农业应用提供了重大进展.
- 可复制的工作流可适应不同地区和数据集,用于自动化映射.
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