Assessment of Component Selection Strategies in Hyperspectral Imagery

@article{IbarrolaUlzurrun2017AssessmentOC,
  title={Assessment of Component Selection Strategies in Hyperspectral Imagery},
  author={Edurne Ibarrola-Ulzurrun and Javier Marcello-Ruiz and Consuelo Gonzalo-Mart{\'i}n},
  journal={Entropy},
  year={2017},
  volume={19},
  pages={666}
}
Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the ’Hughes’ phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to obtain accurate thematic maps. We focus the study on the following feature-extraction algorithms: Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and… CONTINUE READING
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