Remote sensing, which provides inexpensive, synoptic-scale data with multi-temporal coverage, has proven to be very useful in land cover mapping, environmental monitoring, forest and crop inventory, urban studies, natural and man made object recognition, etc. Thematic information extracted from remote sensing imagery is also useful in variety of spatiotemporal applications. However, increasing spatial, spectral, and temporal resolutions invalidate several assumptions made by the traditional classification methods. In this thesis we addressed four specific problems, namely, small training samples, multisource data, aggregate classes, and spatial autocorrelation. We developed a novel semi-supervised learning algorithm to address the small training sample problem. A common assumption made in previous works is that the labeled and unlabeled training samples are drawn from the same mixture model. However, in practice we observed that the number of mixture components for labeled and unlabeled training samples differ significantly. Our adaptive semi-supervised algorithm over comes this important limitation by eliminating unlabeled samples from additional components through a matching process. Multisource data classification is addressed through a combination of knowledge-based and semi-supervised approaches. We solved the aggregate class classification problem by relaxing the unimodal assumption. We developed a novel semi-supervised algorithm to address the spatial autocorrelation problem. Experimental evaluation on remote sensing imagery showed the efficacy of our novel methods over conventional approaches. Together, our research delivered significant improvements in thematic information extraction from remote sensing imagery.