Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields

@article{Gewali2016SpectralAB,
  title={Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields},
  author={Utsav B. Gewali and Sildomar T. Monteiro},
  journal={2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
  year={2016},
  pages={1-6}
}
  • Utsav B. Gewali, Sildomar T. Monteiro
  • Published 2016
  • Computer Science, Mathematics
  • 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
  • In this paper, we propose and compare two spectral angle based approaches for spatial-spectral classification. [...] Key Method The first approach is to use the exponential spectral angle mapper (ESAM) kernel/covariance function, a spectral angle based function, with the support vector machine and the Gaussian process classifier. The second approach is to directly use the minimum spectral angle between the test pixel and the training pixels as the unary energy. We compare the proposed methods with the state-of…Expand Abstract

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