Omid Ghorbanzadeh, Khalil Gholamnia, and Pedram Ghamisi
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 vision. Deep learning algorithms, especially fully convolutional networks (FCNs), and variations like the ResU-Net have been used recently as rapid and automatic landslide detection approaches. Although FCNs have shown cutting-edge results in automatic landslide detection, accuracy can be improved by adding prior knowledge through possible frameworks. This study evaluates a rule-based object-based image analysis (OBIA) approach built on probabilities resulting from the ResU-Net model for landslide detection. We 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 training. In the OBIA stage, we first calculate land cover and image difference indices for pre-and post-landslide multi-temporal images. Next, 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 classification. In 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 alone. Our proposed approach improves the mean intersection-over-union of the resulting map from the ResU-Net by more than 22%.
Big Earth Data, 2022-02-14.