A Regularization-Based Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote Sensing Data

Abstract

AbstractGiven some pixels with user-defined land cover types as labeled positive and negative samples, traditional remote sensing classification methods are sufficient to obtain optimal classification results. However, in many cases, only the positive pixels that users are interested in are labeled, and the negative samples are too diverse to be labeled. Such classification problems are referred to as one-class classification. Traditional learning methods are not suitable for one-class classification problems because labeled negative samples are required for these methods. In this paper, we propose a regularization-based positive and unlabeled learning method called RPUL for one-class classification of high-spatial-resolution aerial photographs. RPUL uses the implicit mixture model of restricted Boltzmann machines (IRBM) as the base framework of the classifier. With the help of a regularization term embedded into the loss function, an additional restriction is imposed on the negative class conditional PDF to ensure that it is as far from the positive class conditional PDF as possible. Thus, although no labeled negative training samples are available, the negative class conditional PDF can be estimated directly to obtain a binary classifier for the detection of the class of interest. The experimental results indicate that the new method provides high classification accuracy and outperforms state-of-the-art methods, including the cost-sensitive positive and unlabeled learning (CSPUL) and Gaussian domain descriptor methods.KeywordsRemote-sensingPositive and unlabeled dataRegularization termRestricted boltzmann machines (RBM)