Thematic classification of multi-spectral remotely sensed imagery for large geographic regions requires complex algorithms and feature selection techniques. Traditional statistical classifiers rely exclusively on spectral characteristics, but thematic classes are often spectrally overlapping. The spectral response distributions of thematic classes are dependent on many factors including terrain, slope, aspect, soil type, and atmospheric conditions present during the image acquisition. With the availability of geo-spatial databases, it is possible to exploit the knowledge derived from these ancillary geo-spatial databases to improve the classification accuracies. However, it is not easy to incorporate this additional knowledge into traditional statistical classification methods. On the other hand, knowledgebased and neural network classifiers can readily incorporate these spatial databases, but these systems are often complex to train and their accuracy is only slightly better than statistical classifiers. In this paper we present a new suit of classifiers developed through NASA funding, which addresses many of these problems and provide a framework for mining multi-spectral and temporal remote sensing images guided by geo-spatial databases.