New approaches are needed to extract useful patterns from increasingly large multi-spectral remote sensing image databases in order to understand global climatic changes, vegetation dynamics, ocean processes, etc. Supervised learning, which is often used in land cover (thematic) classification of remote sensing imagery, requires large amounts of accurate training data. However, in many situations it is very difficult to collect labels for all training samples. In this paper we explore methods that utilize unlabeled samples in supervised learning for thematic information extraction from remote sensing imagery. Our objectives are to understand the impact of parameter estimation with small learning samples on classification accuracy, and to augment the parameter estimation with unlabeled training samples to improve land cover predictions. We have developed a semi-supervised learning method based on the Expectation-Maximization (EM) algorithm, and maximum likelihood and maximum a posteriori classifiers. This scheme utilizes a small set of labeled and a large number of unlabeled training samples. We have conducted several experiments on multi-spectral images to understand the impact of unlabeled samples on the classification performance. Our study shows that though in general classification accuracy improves with the addition of unlabeled training samples, it is not guaranteed to get consistently higher accuracies unless sufficient care is exercised when designing a semi-supervised classifier.