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Remote Sensing Technology and Application  2022, Vol. 37 Issue (5): 1248-1258    DOI: 10.11873/j.issn.1004-0323.2022.5.1248
Optimum Fusion Method and Application of Landslide Remote Sensing in Mountainous Woodland Areas
Yuqing Shi1,2(),Ji Liang1,2(),Yunxing Li3,Saiying Meng1,2,Qian Shi1,2
1.National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology in the Hunan University of Science and Technology,Xiangtan 411201,China
2.School of Geosciences and Spatial Information Engineering,Hunan University of Science and Technology,Xiangtan 411201,China
3.the First Surveying and Mapping Institute of Hunan Province,Changsha 410118,China
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As one of the geological disasters causing huge economic losses and casualties, landslides have attracted more and more attention from society. In order to accurately identify landslide disasters in mountainous woodland areas, the Leijiashan landslide, which occurred on July 6, 2020 in Panping Village, Nanbei Town, Shimen County, Changde City, Hunan Province, was taken as the research object. Different fusion methods such as Principal Component Analysis (PCA), Gram-Schmidt (GS) and Nearest-Neighbor Diffusion (NNDiffuse) are used to fuse the images of Sentinel-1A Interferometric Wide Swath (IW) Ground Range Detected (GRD) image after non-decibelization and decibelization with Sentinel-2A MSI2A image. Through the quality evaluation of the fused image, the PCA fusion method effect of the VV polarization image of Sentinel-1A after decibelization and Sentinel-2A image is the optimal, that is, the optimal fusion image is PCA-VV-DB. The Support Vector Machine (SVM) method was used to identify the landslide of the optimal fusion image (PCA-VV-DB) and the original optical image Sentinel-2A, respectively. Finally, the Sentinel-2A landslide visual interpretation results were used as the inspection standard to evaluate and compare the accuracy of SVM landslide identification results. At the same time, the Shaziba landslide in Mazhe Village, Tunbao Township, Enshi City, Hubei Province, on July 21, 2020, was used as a case to verify the feasibility of this scheme. The results show that compared with the single use of optical image for landslide recognition in the study area, the accuracy of landslide recognition using the optimal fusion image is increased from 95.24% to 96.65%, and the quality of landslide extraction also increased from 87.18% to 91.84%. The leakage recognition and excessive recognition of landslides are reduced, and the research scheme is popularized. It shows that the fusion of optical image and Synthetic Aperture Radar (SAR) image can improve the accuracy of landslide recognition in mountainous woodland areas, and provide valuable information for landslide risk assessment, disaster emergency investigation and disaster recovery and reconstruction.

Key words:  Mountainous woodland areas      Landslide identification      Optical image      SAR image      Fusion      SVM     
Received:  29 July 2021      Published:  13 December 2022
ZTFLH:  TP753  
Corresponding Authors:  Ji Liang     E-mail:;
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Yuqing Shi
Ji Liang
Yunxing Li
Saiying Meng
Qian Shi

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Yuqing Shi,Ji Liang,Yunxing Li,Saiying Meng,Qian Shi. Optimum Fusion Method and Application of Landslide Remote Sensing in Mountainous Woodland Areas. Remote Sensing Technology and Application, 2022, 37(5): 1248-1258.

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Fig.1  Location of study area
Fig.2  Historical weather data from January to July in Shimen County
Sentinel-1A20201108VV+VHIWGRD84(升轨)10 m×10 m
Sentinel-2A202011112、3、4、8S2MSI2A10 m×10 m0.8%
Table 1  Sentinel-1A and Sentinel-2A data parameters
Fig.3  Flow chart of landslide identification technology




Pi 为影像中像素灰度值为i的概率;








Table 2  Evaluation index
Fig.4  Fusion results of S1A GRD and S2A MSI2A images
B82 109.70796.633.60312.911.00————
B82 109.70805.042.94168.39-0.04841.4535.07
B82 109.70800.392.81170.31-0.04824.0335.11
B82 109.70807.143.67218.32-0.06942.3535.00
B82 110.90793.183.65218.37-0.05915.4335.10
B82 105.70815.553.35223.490.15771.0135.91
B82 105.60801.373.29223.990.16750.8336.02
B82 108.80809.653.68243.630.14853.8435.87
B82 106.70782.133.64244.600.15823.0036.08
B82 160.501599.603.59391.220.4786.7833.34
B82 284.402646.403.64641.980.26209.9628.16
B82 109.20782.833.58293.231.0021.0366.54
B82 109.20782.833.58292.961.0021.1566.56
Table 3  Evaluation results of fusion image quality
Fig.5  Results of artificial visual interpretation of landslide in S2A image
Fig.6  SVM landslide recognition results of S2A image and S1A and S2A optimal fusion image



S2A影像0.161 90.015 70.008
最优融合影像0.164 30.008 90.005 796.650.050.0391.84
Table 4  Accuracy of landslide identification
B82 498.70643.980.03213.990.09676.4037.51
Table 5  Evaluation results of fusion image quality
Fig.7  SVM landslide recognition results of S2A image and S1A and S2A optimal fusion image
S2A影像0.274 20.003 90.015 794.580.010.0693.33
最优融合影像0.275 90.004 70.014
Table 6  Accuracy of landslide identification
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