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Bayesian modeling of remote-sensing image content

M. Schröder and K. Seidel and M. Datcu
IEEE Intern. Geoscience and Remote Sensing Symposium IGARSS'99


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.

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  author = {M. Schr\"oder and K. Seidel and M. Datcu},
  title = {Bayesian modeling of remote-sensing image content},
  booktitle = {IEEE Intern. Geoscience and Remote Sensing Symposium IGARSS'99},
  year = {1999},
  keywords = {remote sensing, image indexing, database, Bayesian statistics}