基于无人机图像和点云数据的山地地形变化的多时分析
Tsung-Chin Hou1, Thanh Bao Nhut Trinh2, Tzu-Yi Yang2
1Department of Civil Engineering, National Cheng Kung University, Tainan, Taiwan. tchou@mail.ncku.edu.tw.
Scientific reports
|March 10, 2026
概括
本研究介绍了一种自动化的2D-3D框架,用于在山区检测地形变化. 综合方法提高了监测山体滑坡和管理环境变化的准确性.
科学领域:
- 地质科学 地质科学
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 精确的多时间地形变化检测对于灾害监测和山区环境管理至关重要.
- 挑战包括复杂的地形,密集的植被和封闭,阻碍传统方法.
- 现有的二维方法与阴影和遮蔽作斗争,限制了它们的有效性.
研究的目的:
- 开发一个集成的,自动化的框架,可靠地形变化检测在复杂的山区环境.
- 将二维语义细分和三维几何分析结合起来,以提高准确性.
- 支持灾后评估,山体滑坡监测和基础设施管理.
主要方法:
- 使用的无人机 (UAV) 图像和点云数据.
- 采用DeepLabV3进行多类语义分段的orthophotos.
- 综合快点特征组图 (FPFH),随机样本共识 (RANSAC) 和代最近点 (ICP) 用于几何分析.
- 使用多尺度模型对模型云比较 (M3C2) 度数量化几何变化.
主要成果:
- 在语义细分方面实现了87.05%的欧盟交叉点 (mIoU) 平均值.
- 对于刚性结构的几何记录,获得了4.2厘米的平方根平均误差 (RMSE).
- 与独立验证数据相比,R2 = 0.9251 具有强烈的概括性.
- 2D-3D融合检测到平均2.12米的物理位移,在复杂的区域中超过了仅2D的方法.
结论:
- 拟议的2D-3D融合框架可靠地检测具有挑战性的山区的地形变化.
- 这种综合方法通过克服遮蔽和影子问题,显著改善了仅2D方法.
- 该框架为灾害管理和环境监测中的关键应用提供了一个强大的工具.
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