基于多谱遥感的堤空中图像细分:巴西帕拉州贝洛蒙特水电综合体的一个案例研究
Carlos André de Mattos Teixeira1, Thabatta Moreira Alves de Araujo2, Evelin Cardoso1
1Instituto de Tecnologia, Universidade Federal do Pará, Belém, Pará, Brazil.
PeerJ. Computer science
|June 26, 2025
概括
这项研究使用机器学习和来自无人机 (UAV) 的多光谱数据自动化地岩的视觉检查. 开发的土地覆盖区分方法实现了高精度,改善了水安全监测.
科学领域:
- 地质技术工程 地质技术工程
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 视觉检查对于土岩的稳定性至关重要.
- 通过多谱数据和机器学习进行植被覆盖分析,可以实现自动监测.
- 本研究侧重于大结构的土地覆盖面细分.
研究的目的:
- 引入土岩水土地覆盖区分的方法.
- 评估随机森林 (RF) 模型在划分水结构中的性能.
- 评估非可见光谱带对分类准确性的贡献.
主要方法:
- 利用无人驾驶飞行器 (UAV) 的多谱数据.
- 在手动注释数据上的训练随机森林 (RF) 组合模型.
- 采用特征工程和排名来选择最佳的特征进行细分.
- 使用准确度,卡帕,精度,回忆,F1得分和IOU等指标评估模型性能.
主要成果:
- 通过RF模型实现了对二进制细分的平均IOU的90.9%,对多类细分的平均IOU为91.1%.
- 后处理精制面膜,将平均IOU提高到93.2% (二进制) 和91.9% (多类).
- 证明了非可见带在改善分类方面的有效性.
结论:
- 提出的方法提供了一个灵活的框架,用于像素智能的土地覆盖分类.
- 这种方法是自动化水结构视觉检查的重要一步.
- 自动化监控提高了水安全性,可以减轻故障后果.
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