Combination of one-class remote sensing image classifiers
This paper presents simple but powerful combination methods of dedicated one-class classifiers (OCCs) for efficient remote sensing image classification. The mean and product combination rules are applied to the probabilistic outputs generated by OCCs, and the performance is illustrated in a urban monitoring application in which multi-sensor (optical and SAR) data and multi-source (spectral and contextual) features are available. Two OCCs are used as core parts: the classical mixture of Gaussians (MoG) and the support vector domain description (SVDD) classifier. The obtained results by combining SVDD classifier outputs show a clear improvement in the accuracy, and more robustness to high dimensional samples compared to both MoG and stacked approaches.