基于深度学习的地面透雷达对异质土壤中的树根进行反转
Xibei Li1, Xi Cheng1, Yunjie Zhao2
1School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
一种新的深度学习方法PyViTENet使用地面透雷达 (GPR) 准确地绘制树根系统和土壤结构的图像. 这种非破坏性技术通过详细说明地下异质性,增强了树木健康分析和资源管理.
科学领域:
- 地质物理学 地质物理学
- 生态生态学 生态生态学
- 计算机科学 计算机科学
背景情况:
- 树根对于生态系统健康和资源管理至关重要.
- 准确检测诸如树根之类的地下结构是具有挑战性的.
- 地底透雷达 (GPR) 是一种用于地下成像的非破坏性地质物理方法.
研究的目的:
- 开发基于深度学习的GPR倒置方法,用于实时成像树根和异质土壤结构.
- 为了提高地下材料属性 (允许性) 倒置的准确性和细节性.
- 通过模拟和现实世界GPR数据验证方法的有效性.
主要方法:
- 开发了一个新的深度学习模型,PyViTENet (带有视觉变压器和边缘逆转辅助任务的金字塔卷积网络).
- PyViTENet结合了金字塔卷积和视觉变压器,以增强功能提取.
- 一个边缘反转辅助任务被纳入,以专注于结构细节.
主要成果:
- 在模拟的GPR数据中,PyViTENet在准确反转电容性和土壤分层方面超过了其他深度学习方法.
- 该模型有效地捕捉了树根周围层状土壤的细粒度异质性.
- 使用PyViTENet在测量GPR数据上的转移学习成功重建了散射器信息 (允许性,形状,位置).
结论:
- 拟议的PyViTENet在复杂的地下环境中在GPR逆转方面表现出卓越的性能.
- 该方法提供了高精度和概括能力,用于非破坏性检测地下结构及其周围介质.
- 这项工作为基于GPR的先进生态和地质调查提供了坚实的基础.
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