Weakly supervised landslide detection using medlda regression model

The types of remote sensing data are various but each plays a significant role in the ability to analyze the landslides. To detect landslides with satellite images of two different spatial resolutions in a weakly supervised way, this paper presents a probabilistic topic model - maximum entropy discrimination latent Dirichlet allocation regression (MedLDAr) model, which is a more automated approach compared to our previous method - MedLDA model. A two-step algorithm is used to infer the model. First, before- and after-the event MODIS NDVI productions are employed to get the low-resolution vegetation-cover difference map. Second, MedLDAr model is learned by both of the NDVI difference map (i.e., the weakly supervised information) and post-event SPOT-5 images to detect landslides. Experimental results demonstrate that the proposed method is a more promising and automated way to detect landslides in vegetated regions.