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The application of ResU-net and OBIA for landslide detection from multi-temporal Sentinel-2 images

Big Earth Data 2023年第4期7卷 961-985页

作者： Omid Ghorbanzadeh Khalil Gholamnia Pedram Ghamisi alnstitute of Advanced Research in Artificial Intelligence(IARAl) ViennaAustria Department of Remote Sensing and GIS University of TabrizTabrizIran Machine Learning Group Helmholtz-Zentrum Dresden-RossendorfHelmholtz Institute Freiberg for Resource TechnologyFreibergGermany

landslide detection is a hot topic in the remote sensing community,particularly with the current rapid growth in volume(and variety)of Earth observation data and the substantial progress of computer *** learning algorithms,especially fully convolutional networks(FCNs),and variations like the ResU-Net have been used recently as rapid and automatic landslide detection *** FCNs have shown cutting-edge results in automatic landslide detection,accuracy can be improved by adding prior knowledge through possible *** study evaluates a rulebased object-based image analysis(OBIA)approach built on probabilities resulting from the ResU-Net model for landslide *** train the ResU-Net model using a landslide dataset comprising landslide inventories from various geographic regions,including our study area and test the testing area not used for *** the OBIA stage,we frst calculate land cover and image difference indices for pre-and post-landslide multi-temporal ***,we use the generated indices and the resulting ResU-Net probabilities for image segmentation;the extracted landslide object candidates are then optimized using rule-based *** the result validation section,the landslide detection of the proposed integration of the ResU-Net with a rule-based classification of OBIA is compared with that of the ResU-Net *** proposed approach improves the mean intersection-over-union of the resulting map from the ResU-Net by more than 22%.

关键词： Deep learning(DL) Eastern Iburi Japan European Space Agency(ESA) Fully Convolutional Networks(FCNs) object-based image analysis(OBIA) rapid landslide mapping ResUnet Sentinel-2

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