Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest

@article{Brenning2012SpatialCA,
  title={Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest},
  author={Alexander Brenning},
  journal={2012 IEEE International Geoscience and Remote Sensing Symposium},
  year={2012},
  pages={5372-5375}
}
Novel computational and statistical prediction methods such as the support vector machine are becoming increasingly popular in remote-sensing applications and need to be compared to more traditional approaches like maximum-likelihood classification. However, the accuracy assessment of such predictive models in a spatial context needs to account for the presence of spatial autocorrelation in geospatial data by using spatial cross-validation and bootstrap strategies instead of their now more… CONTINUE READING
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