An efficient spatial semi-supervised learning algorithm

We began by developing a semi-supervised learning method based on the expectation-maximization (EM) algorithm, and maximum likelihood and maximum a posteriori classifiers (MLC and MAP). This scheme utilizes a small set of labeled and a large number of unlabeled training samples. We conducted several experiments on multi-spectral images to understand the impact of unlabeled samples on the classification performance. Our study shows that although, in general, classification accuracy improves with the addition of unlabeled training samples, it is not guaranteed to achieve consistently higher accuracies unless sufficient care is exercised when designing a semi-supervised classifier. We also extended this semi-supervised framework to model spatial context through Markov random fields (MRF). Initial experiments showed an improved accuracy of the spatial semi-supervised algorithm (SSSL) over MLC, semi-supervised, and MRF classifiers. An efficient implementation is provided so that the SSSL can be applied in production environments. We also discuss some open research problems.