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Information Theoretical Assessment of Methods for Segmentation of High Resolution Remote Sensing Images

M. Caparrini, K. Seidel and M. Datcu
IEEE Intern. Geoscience and Remote Sensing Symposium IGARSS 2000
Honolulu, Hawaii, USA, July 2000

Abstract

Scene understanding of remotely sensed images requires a certain amount of preprocessing in order to remove, or alleviate the effects of, all those factors that disturb the imaging process. These factors depend essentially on the peculiar way in which each kind of sensor acquires the image (sensor-related factors) and on the terrain topography, the illumination and the view angle (radiometric factors). In this paper, a Bayesian model-based "maximum a posteriori" estimation approach to correct these disturbing factors is suggested.


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@InProceedings{eth_biwi_00203,
  author = {M. Caparrini and K. Seidel and M. Datcu},
  title = {Information Theoretical Assessment of Methods for Segmentation of High Resolution Remote Sensing Images},
  booktitle = {IEEE Intern. Geoscience and Remote Sensing Symposium IGARSS 2000},
  year = {2000},
  month = {July},
  keywords = {remote sensing,SAR}
}