Efficient algorithm for multispectral data coding using approximate trigonometric expansions

Qurban Memon, Takis Kasparis

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

Abstract

Images obtained from satellite and airborne multispectral collection platforms exhibit a high degree of spatial and spectral correlations that must be properly exploited in any multispectral bandwidth compression scheme. Removing the inherent spectral correlation in the data results in a significant compaction of data to be coded. Discrete approximate trigonometric expansions have previously been proposed for exploiting spatial correlation in 1D signals and images for the purpose of coding.In this paper, we apply the approximate trigonometric expansions to multispectral data, and explore their capability of spectral decorrelation across bands. We show that the compression algorithms employing approximate trigonometric expansions to multispectral imagery provide fast implementation and some how better spectral decorrelation efficiency than discrete cosine transform. For comparison purposes, the results are compared with the techniques employing the discrete cosine transform. Computer simulation results are presented.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSociety of Photo-Optical Instrumentation Engineers
Pages191-202
Number of pages12
ISBN (Print)0819424862
Publication statusPublished - Dec 1 1997
Externally publishedYes
EventAlgorithms for Multispectral and Hyperspectral Imagery III - Orlando, FL, USA
Duration: Apr 22 1997Apr 23 1997

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume3071
ISSN (Print)0277-786X

Other

OtherAlgorithms for Multispectral and Hyperspectral Imagery III
CityOrlando, FL, USA
Period4/22/974/23/97

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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