Corpus ID: 17865664

Principal Component Analysis for Hyperspectral Image Classification

  title={Principal Component Analysis for Hyperspectral Image Classification},
  author={Craig Rodarmel and Jie Shan},
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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An efficient and accurate classification technique for Hyperspectral Images which is very promising in comparison to conventional Support Vector Machine classification which had an overall accuracy of 78.67% with the same data-set. Expand
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A hyperspectral image processing and analysis system (HLPAS) has been developed by the Institute of Remote Sensing Applications of the Chinese Academy of Sciences. The HEAS, built on Interactive DataExpand
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On the mean accuracy of statistical pattern recognizers
  • G. Hughes
  • Mathematics, Computer Science
  • IEEE Trans. Inf. Theory
  • 1968
The overall mean recognition probability (mean accuracy) of a pattern classifier is calculated and numerically plotted as a function of the pattern measurement complexity n and design data set size mExpand