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Texture Information in Remote Sensing Images: A Case Study

M. Schröder and A. Dimai
Workshop on Texture Analysis (WTA'98), Freiburg (Breisgau)
1998

Abstract

In this paper we present a case study on the usage of texture features in optical remote sensing imagery. We compare a Gibbs random field model, rotation-invariant Gabor filters and features based on the grey level co-occurrence matrix (GLCM) for both supervised and un-supervised classification. In the supervised case we present only a qualitative example. In the un-supervised case we use Bayesian classification to determine the inherent structures in the data based on the texture features. A quantitative assessment of the link between these inherent structures and application-oriented content labels concludes the paper.


Download in postscript format
@InProceedings{eth_biwi_00153,
  author = {M. Schr\"oder and A. Dimai},
  title = {Texture Information in Remote Sensing Images: A Case Study},
  booktitle = {Workshop on Texture Analysis (WTA'98), Freiburg (Breisgau)},
  year = {1998},
  keywords = {remote sensing, Bayesian statistics, texture, segmentation}
}