Fast Forward Feature Selection of Hyperspectral Images for Classification With Gaussian Mixture Models

@article{Fauvel2015FastFF,
  title={Fast Forward Feature Selection of Hyperspectral Images for Classification With Gaussian Mixture Models},
  author={Mathieu Fauvel and Cl{\'e}ment Dechesne and Anthony Zullo and Fr{\'e}d{\'e}ric Ferraty},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2015},
  volume={8},
  pages={2824-2831}
}
A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation (k-CV). In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM can be updated when the estimation… CONTINUE READING
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