Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA

Abstract

Landslide detection from extensive remote-sensing imagery is an important preliminary work for landslide mapping, landslide inventories, and landslide hazard assessment. Aimed at development of an automatic procedure for landslide detection, a new method for automatic landslide detection from remote-sensing imagery is presented in this study. We achieved this objective using a scene classification method based on the bag-of-visual-words (BoVW) representation in combination with the unsupervised probabilistic latent semantic analysis (pLSA) model and the k-nearest neighbour (k-NN) classifier. Given a remote-sensing image, we divided it into equal-sized square sub-images and then described each sub-image as a BoVW representation. The pLSA model was applied to sub-images by using the BoVW representation to discover the object classes depicted in the sub-images, and then a k-NN classifier was used to classify the sub-images into landslide areas and non-landslide areas based on object distribution. We investigated the performance and applicability of the method using remote-sensing imagery from the Ili area. The results show that the method is robust and can produce good performance without the acquisition of three-dimensional (3D) topography. We anticipate that these results will be helpful in landslide inventory mapping and landslide hazard assessment in landslide-stricken areas.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (NSFC) under grant numbers 60802084 and by the Northwestern Polytechnical University (NPU) Foundation for Fundamental Research under grant numbers JC200914 and JC201041. We also acknowledge the constructive comments and suggestions provided by reviewers.