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Gibbs Random Field Models for Image Content Characterization

M. Schröder, K. Seidel and M. Datcu
IEEE International Geoscience and Remote Sensing Symposium (IGARSS'97)
1997

Abstract

Satellite images contain an enormous amount of spatial information. To capture that information we propose, in the framework of a stochastic modelling of the image, the use of Gibbs Markov random fields. We expand on a particular model suitable for the use with typical remote sensing images. We demonstrate the capabilities of that model with two examples. In particular, we perform directed queries for specific spatial information.


Download in postscript format
@InProceedings{eth_biwi_00129,
  author = {M. Schr\"oder and K. Seidel and M. Datcu},
  title = {Gibbs Random Field Models for Image Content Characterization },
  booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS'97)},
  year = {1997},
  pages = {258--260},
  volume = {1},
  editor = {T. I. Stein},
  keywords = {remote sensing, image indexing, classification, model-based, segmentation, understanding, database, texture}
}