Query methods using visual semantics play an important role in horizontal interoperability of geospatial databases. However, a common practice is to manually label visual semantics of images using text annotations. This approach is subjective and, more importantly, impractical when dealing with large-scale geospatial image databases. In this paper, we propose a knowledge discovery (KDD) framework to link low-level image features with high-level visual semantics in an attempt to automate the process of retrieving semantically similar images. Our framework first extracts association rules that correlate semantic terms with discrete intervals of individual features. It then applies possibility functions to mathematically model visual semantics. Our approach provides a unique way to query image databases using semantics, and to potentially make available a knowledge exchange method for the geospatial community.