A novel scheme for the compression and classification of hyperspectral images

Bei Xie, Tamal Bose, Erzsébet Merényi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Since hyperspectral images are very large, it is desirable to compress them before transmission. After receiving the compressed image, decompression is applied before performing image classification and other operations. In this paper, a new processing scheme is proposed, where image transform and quantization are applied for image compression at the transmitter and classification is performed directly on the compressed data at the receiver. The advantage of this scheme is that fewer computations are needed. Computer simulations are performed on hyperspectral imagery.

Original languageEnglish (US)
Title of host publicationWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing
DOIs
StatePublished - Dec 21 2009
Externally publishedYes
EventWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - Grenoble, France
Duration: Aug 26 2009Aug 28 2009

Publication series

NameWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

Other

OtherWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
CountryFrance
CityGrenoble
Period8/26/098/28/09

Keywords

  • Hyperspectral imagery
  • Image classification
  • Lossy data compression
  • Neural network

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Xie, B., Bose, T., & Merényi, E. (2009). A novel scheme for the compression and classification of hyperspectral images. In WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing [5289075] (WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing). https://doi.org/10.1109/WHISPERS.2009.5289075