Approaches to Classification of Multichannel Images

  • Vladimir Lukin
  • Nikolay Ponomarenko
  • Andrey Kurekin
  • Kenneth Lever
  • Oleksiy Pogrebnyak
  • Luis Pastor Sanchez Fernandez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

The comparison of different approaches to classification of multichannel remote sensing images obtained by spaceborne imaging systems is presented. It is demonstrated that it is reasonable to compress original noisy images with appropriate compression ratio and then to classify the decompressed images rather than original data. Two classifiers are considered: based on radial basis function neural network and support vector machine. The latter one produces slightly better classification results.

Keywords

Multichannel image classification image compression remote sensing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Vladimir Lukin
    • 1
  • Nikolay Ponomarenko
    • 1
  • Andrey Kurekin
    • 2
  • Kenneth Lever
    • 2
  • Oleksiy Pogrebnyak
    • 3
  • Luis Pastor Sanchez Fernandez
    • 3
  1. 1.Dept of Transmitters, Receivers and Signal ProcessingNational Aerospace UniversityKharkovUkraine
  2. 2.Department of Computer Science, School of EngeneeringCardiff UniversityCardiffUK
  3. 3.Centro de Investigacion en ComputacionInstituto Politecnico NacionalMexico D.F.Mexico

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