一种基于深度学习的算法,用于在广的区域中检测山体滑坡,使用考虑地形特征的InSAR图像.
Ning Li1, Guangcai Feng1, Yinggang Zhao1
1School of Geosciences and Info-Physics, Central South University, Changsha 410083, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究介绍了MSFD-Net,这是一种新型的深度学习模型,它融合了交叉测量合成孔径雷达 (InSAR) 和地形数据,用于准确地检测山体滑坡,识别了中国的254起山体滑坡.
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:
- 由于人类活动和环境变化,山体滑坡带来了重大危险.
- 干涉测量合成孔径雷达 (InSAR) 对于检测表面变形和启用滑坡早期预警至关重要.
- 目前使用InSAR数据进行山体滑坡检测的深度学习模型通常由于依赖单个数据源而受到有限的准确性影响.
研究的目的:
- 通过整合多源数据,提高滑坡识别的准确性.
- 开发一个改进的地质危险检测模型,用于精确地绘制山体滑坡地图.
- 解决现有方法在区分山体滑坡和其他地质危险方面的局限性.
主要方法:
- 提出了一个使用伪语架构的多源数据融合网络 (MSFD-Net).
- 从InSAR变形数据中提取了纹理特征和从海拔数据中提取了地形特征.
- 网络中更高层次的融合特征用于全面分析.
主要成果:
- 在比较和废弃实验中,MSFD-Net表现出卓越的性能.
- 通过使用 Sentinel-1 SAR 数据 (2018-2020年) 成功识别了黄河地区的254起山体滑坡.
- 检测到的山体滑坡主要发生在海拔2500-3700米处,坡度为10-30°.
结论:
- 拟议的MSFD-Net通过融合InSAR和地形数据,有效地提高了山体滑坡检测的准确性.
- 这种方法为快速准确地检测大面积的山体滑坡提供了有前途的方法.
- 促进及时实施防护和控制措施的山体滑坡危险.
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