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

[1]	许强, 董秀军, 李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报(信息科学版), 2019, 44(7):957-966.
[1]	Xu Q, Dong X J, Li W L. Integrated space-air-ground early detection,monitoring and warning system for potential catastrophic geohazards[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7):957-966.
[2]	李松, 李亦秋, 安裕伦. 基于变化检测的滑坡灾害自动识别[J]. 遥感信息, 2010(1):27-31.
[2]	Li S, Li Y Q, An Y L. Automatic recognition of landslides based on change detection[J]. Remote Sensing Information, 2010(1):27-31.
[3]	张帅娟. 变化检测和面向对象结合的高分辨率遥感影像滑坡体提取方法研究[D]. 成都: 西南交通大学, 2017.
[3]	Zhang S J. Research on landslide body extraction method from high-resolution remote sensing image based on change detection and object-oriented[D]. Chengdu: Southwest Jiaotong University, 2017.
[4]	周志华, 林维芳, 许高程, 等. 基于面向对象的滑坡快速识别技术研究[J]. 安徽农业科学, 2012, 40(5):3017-3018,3071.
[4]	Zhou Z H, Lin W F, Xu G C, et al. Research of fast landslide recognition based on object-oriented technology[J]. Journal of Abhui Agricultural Sciences, 2012, 40(5):3017-3018,3071.
[5]	丁辉, 张茂省, 朱卫红, 等. 黄土滑坡高分辨率遥感影像识别——以陕西省延安市地区为例[J]. 西北地质, 2019, 52(3):231-239.
[5]	Ding H, Zhang M S, Zhu W H, et al. High resolution remote sensing for the identification if loess landslides:Example from Yan’an City[J]. Northwestern Geology, 2019, 52(3):231-239.
[6]	Ye C M, Li Y, Cui P, et al. Landslide detection of hyperspectral remote sensing data based on deep learning with constrains[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(12):5047-5060. doi: 10.1109/JSTARS.2019.2951725 url: https://ieeexplore.ieee.org/document/8911205/
[7]	Ghorbanzadeh O, Blaschke T, Gholamnia K, et al. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection[J]. Remote Sensing, 2019, 11(2):1-21. doi: 10.3390/rs11010001 url: http://www.mdpi.com/2072-4292/11/1/1
[8]	Wang H J, Zhang L M, Yin K S, et al. Landslide identification using machine learning[J]. Geoscience Frontiers, 2020, 12(1):351-364. doi: 10.1016/j.gsf.2020.02.012 url: https://linkinghub.elsevier.com/retrieve/pii/S1674987120300542
[9]	巨袁臻, 许强, 金时超, 等. 使用深度学习方法实现黄土滑坡自动识别[J]. 武汉大学学报(信息科学版), 2020, 45(11):1747-1755.
[9]	Ju Y Z, Xu Q, Jin S C, et al. Automatic object detection of loess landslide based on deep learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11):1747-1755.
[10]	Ji S P, Yu D W, Shen C Y, et al. Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks[J]. Landslides, 2020:1-16.
[11]	Yu B, Chen F, Xu C. Landslide detection based on contour-based deep learning framework in case of national scale of Nepal in 2015[J]. Computers & Geosciences, 2020, 135:104388. doi: 10.1016/j.cageo.2019.104388 url: https://linkinghub.elsevier.com/retrieve/pii/S0098300419303097
[12]	Ullo S L, Mohan A, Sebastianelli A, et al. A new Mask R-CNN-based method for improved landslide detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:3799-810. doi: 10.1109/JSTARS.2021.3064981 url: https://ieeexplore.ieee.org/document/9373966/
[13]	Cheng L, Li J, Duan P, et al. A small attentional YOLO model for landslide detection from satellite remote sensing images[J]. Landslides, 2021, 18(8):2751-65. doi: 10.1007/s10346-021-01694-6 url: https://doi.org/10.1007/s10346-021-01694-6
[14]	Catani F. Landslide detection by deep learning of non-nadiral and crowdsourced optical images[J]. Landslides, 2021, 18(3):1025-44. doi: 10.1007/s10346-020-01513-4 url: https://doi.org/10.1007/s10346-020-01513-4
[15]	Hacıefendioğlu K, Demir G, Başağa H B. Landslide detection using visualization techniques for deep convolutional neural network models[J]. Natural Hazards, 2021, 109(1):329-50. doi: 10.1007/s11069-021-04838-y url: https://doi.org/10.1007/s11069-021-04838-y

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