Journal of Yangtze River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (1): 184-190.DOI: 10.11988/ckyyb.20210729


Landslide Information Extraction by Fusion of Hyperspectral and Radar Data

LI Xiao-lai1, LI Hai-tao1, YANG Shi-qiang1, XU Hai-zhang1, WANG Qing2   

  1. 1. Maintenance Company of State Grid Hubei Electric Power Co., Ltd., Yichang 443300, China;
    2. School of Earth Sciences,Yangtze University, Wuhan 430100, China
  • Received:2021-07-19 Revised:2021-12-14 Online:2023-01-01 Published:2023-02-24

Abstract: The aim of this research is to enhance the extraction accuracy by improving the classification of micro-terrain landslide remote sensing information. The landslide information in local areas of Yichang was extracted by using the method of Convolutional Neural Networks (CNN) combined with Convolutional Block Attention Module (CBAM) based on the fusion of Unmanned Aerial Vehicle (UAV) hyperspectral image (HSI) and Light Detection and Ranging (LiDAR) data. Results demonstrated that landslide information can be extracted with more accuracy based on the advantages of hyperspectral and radar data.

Key words: hyperspectral image, LiDAR, data fusion, CBAM, landslide information extraction

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