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Bayesian Methods: Applications in Information Aggregation and Image Data Mining

M. Datcu and K. Seidel
EARSeL/IAPRS, International Archives of Photogrammetry and Remote Sensing, Workshop, Valladolid
1999

Abstract

More accurate interpretation of remotely sensed data is based on a concept combining synergistically signals, information or knowledge from different sources. The aim is information mining, extraction and presentation. A hierarchical structure of data fusion levels has been identified: on image signal level, on image features, on physical parameters extracted from images, on meta features resulting from image feature modelling, on feature grouping. The Bayesian perspective is discussed aiming at a variety of aspects. The power of the Bayesian approach is endowed i. e. by the possibility to analyse uniformly the uncertainties over scene parameters in data acquired from heterogeneous and incommensurable sources.


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@InProceedings{eth_biwi_00185,
  author = {M. Datcu and K. Seidel},
  title = {Bayesian Methods: Applications in Information Aggregation and Image Data Mining},
  booktitle = {EARSeL/IAPRS, International Archives of Photogrammetry and Remote Sensing, Workshop, Valladolid},
  year = {1999},
  pages = {68--73},
  volume = {32},
  editor = {E.P. Baltsavias and B. Csatho and M. Hahn and B. Koch and A. Sieber and L. Wald and D. Wang},
  keywords = {remote sensing, data fusion, information mining, Bayesian methods}
}