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Gibbs random field models: a toolbox for spatial information extraction

M. Schröder, M. Walessa, H. Rehrauer, K. Seidel and M. Datcu
Computers and Geosciences 26
Vol. 26, pp. 423--432, 2000

Abstract

In this paper, we present Gibbs random field models in the form of a powerful toolbox for spatial information extraction from remote sensing images. These models are defined via parametrised energy functions that characterise local interactions between neighbouring pixels. After shortly revisiting the information theoretical concept and defining a family of Gibbs models, we give a tour through examples of different kinds of spatial information extraction. These examples range from parameter estimation and analysis, via selection of the model that best describes the image data, up to the segmentation of the whole image i11to regions with uniform properties of the model. Finally, the concept of across-image segmentation of spatial information leads to an application for contentbased queries from remote sensing image archives.


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@Article{eth_biwi_00210,
  author = {M. Schr\"oder and M. Walessa and H. Rehrauer and K. Seidel and M. Datcu},
  title = {Gibbs random field models: a toolbox for spatial information extraction},
  journal = {Computers and Geosciences 26},
  year = {2000},
  month = {},
  pages = {423--432},
  volume = {26},
  number = {},
  keywords = {remote sensing, image processing, image segmentation}
}