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Information theoretical assessment of methods for snow cover segmentation from high resolution remote sensing images

M. Caparrini, K. Seidel and M. Datcu
Proceedings of the 20th EARSeL Symposium on Remote Sensing
Dresden, Germany, June 2000

Abstract

In this paper, a simulation is performed to demonstrate the feasibility of a Bayesian model-based MAP-estimation approach to simultaneous radiometric corrections and backscatter parameter estimation on remote sensing images. The final goal is to define a procedure for a high precision MAP segmentation of images and a concurrent improvement of the relative DEMs, given a parametric model of the backscatter function , exploiting synergetically both the image and the DEM.


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@InProceedings{eth_biwi_00204,
  author = {M. Caparrini and K. Seidel and M. Datcu},
  title = {Information theoretical assessment of methods for snow cover segmentation from high resolution remote sensing images},
  booktitle = {Proceedings of the 20th EARSeL Symposium on Remote Sensing},
  year = {2000},
  month = {June},
  editor = {S. Wunderle},
  keywords = {MAP estimation, information extraction, DEM}
}