Extraction Of Built-up Areas From Remote Sensing Imagery Using One-Class Classification

Mapping of built-up areas were always a main concern to researchers in the field of remote sensing. Thus, several techniques have been proposed to saving technicians from digitizing hundreds of areas by hand. Multiclass classifiers exhibit a very promising performance in terms of classification accuracy. However, they require that all classes in the study area to be labeled. In many applications, users may only be interested in a specific land class. This referred to as one-class classification (OC) problem. In this paper, we compare a Binary Support Vector Machine (BSVM) classifier, with two OC classifiers, OC SVM (OCSVM), and Presence and Background Learning (PBL) framework for the extracting built-up areas from Gaofen-2 and Aster satellites imagery. The obtained classification accuracies show that PBL provides competitive extraction results due to the fact that PBL is a positive-unlabeled method based on neural network in which large amounts of available unlabeled samples is incorporated into the training phase, allowing the classifier to model the built-up class more effectively.