Image Classification with Semi-Supervised One-Class Support Vector Machine

摘要

This paper presents a semi-supervised one-class support vector machine classifier for remote sensing applications. In one-class image classification, one tries to detect pixels belonging to one class and reject the others. When few labeled target pixels and no labeled outlier pixels are available, the selection of the support vector machine free parameters is very challenging. This problem can be alleviated by introducing the information of the wealth of unlabeled samples present in the scene. The proposed algorithm deforms the training kernel by modelling the data marginal distribution with the graph Laplacian built with labeled and unlabeled samples. The good performance of the proposed method is illustrated in challenging remote sensing image classification scenarios where information of only one class of interest is available. In particular, we present results in multispectral cloud screening, hyperspectral crop detection, and multisource urban monitoring. Experimental results show the suitability of the proposal, specially in cases with few or poorly representative labeled samples.