CTHNet:一个CNN-变压器混合网络用于洛斯高原地区的山体滑坡识别,使用高分辨率遥感图像
Juan Li1,2, Jin Zhang1, Yongyong Fu3
1College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
一个新的CNN-变压器混合网络有效地识别了中国洛斯高原的山体滑坡. 这种先进的模型提高了复杂地形的山体滑坡检测准确度,有助于灾害管理.
科学领域:
- 地质科学 地质科学
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
背景情况:
- 洛斯高原的碎片化地形增加了山体滑坡的风险.
- 卷积神经网络 (CNN) 在复杂环境中难以检测山体滑坡,导致错误.
- 高分辨率的遥感图像对于山体滑坡监测至关重要.
研究的目的:
- 开发一个改进的深度学习模型,用于在Loess高原准确地识别山体滑坡.
- 在复杂的地形分析中克服传统CNN的局限性.
- 为了增强自动发现滑坡的灾难管理.
主要方法:
- 提出了一个混合CNN-变压器神经网络架构.
- 创建了一个包含1500个Loess滑坡和非滑坡样本的数据库.
- 该模型使用CNN提取本地特征,使用变压器模块提取全球特征.
主要成果:
- 在复杂的环境中,CNN-变压器混合网络展示了有效的山体滑坡检测.
- 与U-Net,HCNet和TransUNet相比,提出的模型获得了较高的F1得分,改善了至少3.81%.
- 该模型成功地以更高的精度识别了山体滑坡的位置和范围.
结论:
- 开发的混合网络为洛斯高原的山体滑坡识别提供了更准确,更智能的解决方案.
- 这项技术在山体滑坡调查,风险评估和灾害管理方面具有重要的实际应用.
- 该研究在使用遥感数据的自动化滑坡监测领域取得了进展.
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