Hyperspectral dimension reduction using global and local information based linear discriminant analysis

@inproceedings{Sakarya2014HyperspectralDR,
  title={Hyperspectral dimension reduction using global and local information based linear discriminant analysis},
  author={Ufuk Sakarya},
  year={2014}
}
Hyperspectral image classification has become an important research topic in remote sensing. Because of high dimensional data, a special attention is needed dealing with spectral data; and thus, one of the research topics in hyperspectral image classification is dimension reduction. In this paper, a dimension reduction approach is presented for classification on hyperspectral images. Advantages of the usage of not only global pattern information, but also local pattern information are examined… CONTINUE READING

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