Landslide Image Classification Using Semi-Supervised Learning

Abstract

Many researchers focus on the problem of accurately and rapidly classifying regional landslide hazard, whose spectrum and shape are complicated and varied in remote sensing images. A large number of training examples with labels are necessary to construct predictive models in supervised classification, which are difficult to get strong supervision information due to the high cost of data labeling process for rapid regional landslides identification. Our methods use pre- and post-event MODIS NDVI products, and post-event SPOT-5 images to classify the landslide image during the 2008 Wenchuan Earthquake based on semi-supervised learning model, which means that only a subset of training data are given with labels to train a good learner. To examine the effectiveness of the proposed method, the results are compared with state-of-the-art support vector machine (SVM). Experimental results demonstrate that the proposed method is an accurate and rapid way to classify landslide images.