通过机器与机器对抗地形特征. 深度学习使用遥感
Jordan Ewing1, Thomas Oommen1, Jobin Thomas1
1Department of Geological Engineering, Michigan Technological University, Houghton, MI 49931, USA.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
无人飞行器 (UAV) 的遥感与深度学习相结合,为预测土壤水分和强度等地形特性提供了更快,更安全的方法. 这种方法增强了地面车辆的移动性绘图,提高了任务的成功.
科学领域:
- 地质科学 地质科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 地形可行性对于地面车辆的移动性和任务的成功至关重要.
- 目前在现场进行的土壤测量是耗时的,昂贵的和潜在的危险的.
- 遥感为地形财产评估提供了一个潜在的替代方案.
研究的目的:
- 研究基于无人机的遥感 (热,多光谱,超光谱) 用于地形属性预测的应用.
- 为了比较机器学习和深度学习算法在估计土壤水分和强度方面的有效性.
- 开发快速,经济高效,更安全的流动性测绘方法.
主要方法:
- 利用来自无人机平台的热,多光谱和高光谱遥感数据.
- 应用了各种机器学习 (线性,,拉索,PLS,SVM,KNN) 和深度学习 (MLP,CNN) 算法.
- 预测的土壤特性 (湿度,透仪强度) 与车辆性能指标 (车轮滑动,速度) 相关联.
主要成果:
- 深度学习模型显著超过了传统的机器学习模型.
- 多层感知器 (MLP) 在预测土壤水分 (R2=0.97) 和土壤强度 (CP06:R2=0.95,CP12:R2=0.92) 方面取得了最高的准确性.
- 使用Polaris MRZR.使用预测的土壤强度和车辆性能 (轮滑,速度) 之间的相关性观察.
结论:
- 基于无人机的遥感与深度学习相结合,为地形分析的现场测量提供了可行,高效和安全的替代方案.
- 开发的预测地图可以有效地应用于增强地面车辆移动性评估.
- 这种方法对于需要快速地形评估的军事和民用应用具有重大潜力.
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