Landslide identification using remote sensing images and DEM based on convolutional neural network: A case study of loess landslide

doi:10.6046/zrzyyg.2021204

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Abstract

China is one of the countries with frequent landslide disasters. In recent years. In recent years, more than 70% of the catastrophic geological hazards have occurred not within the scope of known hidden danger points of geological hazards in China. Therefore, there is an urgent need for investigating large-scale landslide disasters using automatic and efficient technologies and methods for landslide identification. To quickly identify the location of landslides from massive remote sensing images, it is necessary to determine the key areas of landslides to support subsequent interpretation and research. This study investigated loess landslide identification based on GF-1 images and digital elevation model (DEM) data. First, a database of remote sensing images and DEM landslide samples was constructed. Second, the landslide samples were classified using the channel fusion convolutional neural network model. Finally, the classification results were restored to the remote sensing images according to the location information. Experimental results showed that the model yielded landslide identification accuracy of 95.7% and a recall rate of 100.0%. The model used in this study has a small number of network layers, a high convergence speed, and higher efficiency and identification accuracy. As a result, it allows for the quick identification of key landslide areas from remote sensing images in the case of a limited number of samples, thus supporting the investigation of large-scale landslide disasters.

Keywords convolutional neural network landslide identification remote sensing classification DEM loess landslide

ZTFLH:	TP751
	TP399

Corresponding Authors: HAN Lingyi E-mail: zhaoyingzhaoting@163.com;hanlingyi@mail.cgs.gov.cn

Issue Date: 20 June 2022

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	Zhaoying YANG
	Lingyi HAN
	Xiangxiang ZHENG
	Wenji LI
	Lei FENG
	Yi WANG
	Yongpeng YANG

Cite this article:

Zhaoying YANG,Lingyi HAN,Xiangxiang ZHENG, et al. Landslide identification using remote sensing images and DEM based on convolutional neural network: A case study of loess landslide[J]. Remote Sensing for Natural Resources, 2022, 34(2): 224-230.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021204 OR https://www.gtzyyg.com/EN/Y2022/V34/I2/224

Fig.1 Typical landslide examples

Fig.2 Typical landslide examples

Fig.3 Model structure diagram

Tab.1 Training and test sample statistics table

Fig.4 Experiment 1 training accuracy and loss

Fig.5 Experiment 2 training accuracy and loss

Tab.2 Test results of experiment 1 and experiment 2

Fig.6 Results of landslide recognition

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