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Interactive Learning and Probabilistic Retrieval in Remote Sensing Image Archives

M. Schröder, H. Rehrauer, K. Seidel and M. Datcu
IEEE Trans. on Geoscience and Remote Sensing 38
Vol. 38, No. 5, pp. 2288--2298, September 2000


We present a concept of interactive learning and probabilistic retrieval of user-specific cover-types in a content based remote sensing image archive. A cover-type is incrementally defined via user-provided positive and negative examples. From these examples we infer probabilities of the Bayesian network that links the user interests to a pre-extracted content index. Due to the stochastic nature of the cover-type definitions, the database system not only retrieves images according to the estimated coverage, but also according to the accuracy of that estimation given the current state of learning. For the latter, we introduce the concept of separability. We expand on the steps of Bayesian inference to compute the application-free content index using a family of data models, and on the description of the stochastic link using hyper-parameters. In particular, we focus on the interactive nature of our approach, which provides instantaneous feedback to the user in the form of an immediate update of the posterior map, and a very fast, approximate search in the archive. A java-based demonstrator using the presented concept of content-based access to a test archive of Landsat-TM, X-SAR, and aerial images is available over the Internet [].

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  author = {M. Schr\"oder and H. Rehrauer and K. Seidel and M. Datcu},
  title = {Interactive Learning and Probabilistic Retrieval in Remote Sensing Image Archives},
  journal = {IEEE Trans. on Geoscience and Remote Sensing 38},
  year = {2000},
  month = {September},
  pages = {2288--2298},
  volume = {38},
  number = {5},
  keywords = {remote sensing, information theory, GMRF}