Landslide detection with two satellite images of different spatial resolutions in a probabilistic topic model

As the most commonly techniques to landslide inventory mapping, visual interpretation and geomorphological field surveys are time-consuming and labor-intensive. In this paper, a probabilistic topic model, maximum entropy discrimination latent Dirichlet allocation (MedLDA), is presented to detect landslides with satellite images of two different spatial resolutions in a weakly supervised way. A two-stage algorithm is inferred the model. First, before- and after- the event MODIS NDVI productions are employed to roughly locate probable landslides, i.e., low-resolution vegetation-cover changes. Second, MedLDA model is learned by both NDVI change values (i.e., the weakly supervised information) and post-event SPOT 5 images to detect the landslide. Experimental results demonstrate that the proposed method is a very promising way to detect landslides in vegetated regions.