The Effect of Sub-sampling on Hyperspectral Dimension Reduction

  • Ali Ömer Kozal
  • Mustafa Teke
  • Hakkı Alparslan Ilgın
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 210)


Hyperspectral images which are captured in narrow bands in continuous manner contain very large data. This data need high processing power to classify and may contain redundant information. A variety of dimension reduction methods are used to cope with this high dimensionality. In this paper, the effect of sub-sampling hyperspectral images for dimension reduction techniques is explored and compared in classification performance and calculation time.


Hyperspectral imaging dimension reduction remote sensing hyperspectral image classification 


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Ali Ömer Kozal
    • 1
    • 2
  • Mustafa Teke
    • 1
  • Hakkı Alparslan Ilgın
    • 2
  1. 1.TÜBİTAK UZAY (The Scientific And Technological Research Council Of Turkey, Space Technologies Research Institute)ODTÜ YerleşkesiAnkaraTurkey
  2. 2.Electrical and Electronics Eng. Dept.Ankara UniversityAnkaraTurkey

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