The new generation of high resolution imaging satellites acquires huge amounts of data which are stored in large archives. The state-of-the-art systems for data access allow only queries by geographical location, time of acquisition or type of sensor. This information is often less important than the content of the scene, i.e. structures, objects or scattering properties. Meanwhile, many new applications of remote sensing data are closer to computer vision and require the knowledge of complicated spatial and structural relationships among image objects. We are creating an intelligent satellite information mining system, a next generation architecture to help users to gather rapidly information during courses of actions, a tool to add value and to manage the huge amount of historical and newly acquired satellite data-sets by giving to experts access to relevant information in an understandable and directly usable form and to provide friendly interfaces for information query and browsing. Research topics are within the frame of Baysian learning, content-based querying, data modelling, adaptation to user conjecture.