Subpixel Urban Land Cover Estimation: Comparing Cubist, Random Forests, and Support Vector Regression

@inproceedings{Walton2008SubpixelUL,
  title={Subpixel Urban Land Cover Estimation: Comparing Cubist, Random Forests, and Support Vector Regression},
  author={Jeffrey T. Walton},
  year={2008}
}
Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (reflectance, tasseled cap, and both reflectance and tasseled cap plus thermal) were compared for their effectiveness with each of the… CONTINUE READING
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