中国西南山区植被茂盛,该区域光学遥感影像上的滑坡常被植被遮挡、难以辨识,基于光学遥感影像的植被覆盖滑坡识别错误率较高,难以满足实际需求.针对这一问题,利用机载激光雷达(light detection and ranging,LiDAR)生成的数字高程模型(digital elevation model,DEM)和山体阴影图去除滑坡表面的植被覆盖,构建了一个植被覆盖山区的滑坡数据集.在此基础上,提出一种基于多模态深度学习的智能滑坡识别模型,综合利用DEM和山体阴影图识别植被覆盖条件下的滑坡,模型主要包括3个神经网络模块:自动提取DEM数据特征的Transformer神经网络,自动提取山影图特征的Transformer神经网络,以及融合多模态遥感数据的卷积注意力神经网络.实验对比了 ResU-Net、LandsNet、HRNet、SeaFormer模型,结果表明,所提模型达到了最高的滑坡预测精度,交并比和F1值分别提高了 9.3％和6.8％.因此,Li-DAR能够有效地去除植被干扰,适用于识别西南山区植被覆盖条件下的滑坡;提出的LiDAR滑坡识别模型能够预测滑坡的位置,为滑坡监测设备选址提供了有力支撑.
A Multi-modal Deep Neural Network Model for Forested Landslide Detection
Objectives:Vegetation widely spread in the southwestern mountainous regions of China.In the remote sensing images of this area,the landslides are usually shaded by vegetation.The error rate of forested landslide detection in remote sensing images is high,which is hard to meet practical needs.Methods:To address this issue,this paper uses light detection and ranging(LiDAR)-derived digital elevation mode(DEM)and hillshade to remove the forest on the landslides.In addition,a new dataset for forested landslide detection is also constructed.On this basis,an intelligent landslide detection model base on multi-modal deep learning is proposed.The proposed model uses DEM and hillshade to identify forested landslides,which consists of three neural network models:A transformer network for automatically extracting DEM fea-tures,a transformer network for automatically extracting hillshade features,and a convolution neural net-work with attention mechanism for merging multi-modal remote sensing data.Results:The proposed model is compared with ResU-Net,LandsNet,HRNet and SeaFormer.Experimental results show that the pro-posed model achieves the highest prediction accuracy.Intersection over union and F1 are improved by 9.3％ and 6.8％ ,respectively.Conclusions:LiDAR is able to remove the impact of forest cover,which is suitable for identifying the forested landslides in the southwest mountain areas of China.The proposed LiDAR-based landslide detection model is able to predict the position of landslides,which is useful for deciding the position of landslide monitoring devices.
landslide detectionvegetation coverhillshadeDEMmulti-modal deep learningneural network model
万方数据
维普
