Bayesian Modeling of Remote Sensing Image Content

Remote Sensing images exhibit an enormous amount of information. In order to extract this information in a robust way and to make it available as efficient indices for query by image content, we present a scheme of hierarchical stochastic description. The different levels in this hierarchy are derived from the different levels of abstraction: image data (0), image features (1), meta features (2), image classification (3), geometric features (4), and user-specific semantics (5). We describe this hierarchical scheme and the processes of Bayesian inference between these levels and present a case study using synthetic aperture radar (SAR) data.