Feature selection of hyperspectral data through local correlation and SFFS for crop classification

@article{GmezChova2003FeatureSO,
  title={Feature selection of hyperspectral data through local correlation and SFFS for crop classification},
  author={Luis G{\'o}mez-Chova and Javier Calpe and Gustavo Camps-Valls and J. D. Martin and E. Soria and Joan Vila and Luis Alonso-Chorda and Jos{\'e} Francisco Quesada Moreno},
  journal={IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477)},
  year={2003},
  volume={1},
  pages={555-557 vol.1}
}
In this paper, we propose a procedure to reduce dimensionality of hyperspectral data while preserving relevant information for posterior crop cover classification. One of the main problems with hyperspectral image processing is the huge amount of data involved. In addition, pattern recognition methods are sensitive to problems associated to high dimensionality feature spaces (referred to as Hughes phenomenon of curse of dimensionality). We propose a dimensionality reduction strategy that… CONTINUE READING

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