Semi-supervised change detection via Gaussian processes

This paper introduces a semi-supervised change detection method that exploits both labeled and unlabeled samples via Gaussian Process (GP). The proposed method is based on recent development in Gaussian Process classifier named NCNM [3]. NCNM is a probabilistic approach to learning a GP classifier in the presence of unlabeled data. It involves a novel transductive learning under a probabilistic framework. Experimental results obtained on two sets of multitemporal remote sensing images confirm the effectiveness of the proposed approach. It also proves that NCNM can compete seriously with the state-of-the-art support vector machines (SVM) classifier for remote sensing image change detection.