Corpus ID: 17865664

Principal Component Analysis for Hyperspectral Image Classification

@inproceedings{Rodarmel2002PrincipalCA,
  title={Principal Component Analysis for Hyperspectral Image Classification},
  author={Craig Rodarmel and Jie Shan},
  year={2002}
}
The availability of hyperspectral images expands the capability of using image classification to study detailed characteristics of objects, but at a cost of having to deal with huge data sets. This work studies the use of the principal component analysis as a preprocessing technique for the classification of hyperspectral images. Two hyperspectral data sets, HYDICE and AVIRIS, were used for the study. A brief presentation of the principal component analysis approach is followed by an… Expand
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