|Table of Contents|

Convolutional Neural Network Landslide Recognition Based on Terrain Feature Fusion(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2022年第03期
Page:
568-579
Research Field:
大地测量、遥感与地学大数据
Publishing date:

Info

Title:
Convolutional Neural Network Landslide Recognition Based on Terrain Feature Fusion
Author(s):
CAI Hao-jie1; 2;  HAN Hai-hui1; 2;  ZHANG Yu-lian1;  WANG Li-she1
(1. Xi'an Center of Geological Survey, China Geological Survey, Xi'an 710054, Shaanxi, China; 2. Remote Sensing Application Branch for High-quality Development of the Yellow River Basin, China Association of Remote Sensing Application, Xi'an 710054, Shaanxi, China)
Keywords:
geological hazard;  landslide recognition;  convolutional neural network;  remote sensing image;  terrain factor;  deep learning;  feature fusion;  Sichuan
PACS:
P642.22; TP183
DOI:
10.19814/j.jese.2021.12016
Abstract:
Landslides seriously threaten the safety of people's lives and property. A complete and accurate landslide inventory map is important for the study on landslides. The deep convolutional neural network method has attracted great attention due to its numerous advantages. However, the convolutional neural network has a complex structure and requires lots of training samples, which restrict the development of this technology in landslide recognition. A convolutional neural network modeling method incorporating terrain features was proposed. Firstly, a new landslide sample is constructed by superimposing terrain factors on remote sensing images, then a lightweight convolutional neural network that extracts and fuses spatial and spectral features(FF-CNN)is designed, and finally the optimal model is trained for landslide recognition. The ablation experiments in Wenchuan area of Sichuan show that after fusing spectral features on the basis of spatial features, the F1 score and MIoU of FF-CNN model are increased by 0.020 2 and 0.014 4, respectively; after superimposing the terrain factor on the remote sensing image, the F1 score and MIoU of FF-CNN model are increased by 0.066 4 and 0.048 2, respectively. The experiments in Three Gorges reservoir area of Hubei province and Hongkou town of Dujiangyan city, Sichuan province, show that FF-CNN model has strong applicability, migration ability and great potential in landslide recognition.

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Last Update: 2022-06-01