Hyperspectral image compression using an online learning method

Irem Ülkü*, B. Uʇur Töreyin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

A hyperspectral image compression method is proposed using an online dictionary learning approach. The online learning mechanism is aimed at utilizing least number of dictionary elements for each hyperspectral image under consideration. In order to meet this "sparsity constraint", basis pursuit algorithm is used. Hyperspectral imagery from AVIRIS datasets are used for testing purposes. Effects of non-zero dictionary elements on the compression performance are analyzed. Results indicate that, the proposed online dictionary learning algorithm may be utilized for higher data rates, as it performs better in terms of PSNR values, as compared with the state-of-the-art predictive lossy compression schemes.

Original languageEnglish
Title of host publicationSatellite Data Compression, Communications, and Processing XI
EditorsYunsong Li, Chein-I Chang, Bormin Huang, Qian Du, Chulhee Lee
PublisherSPIE
ISBN (Electronic)9781628416176
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventSatellite Data Compression, Communications, and Processing XI - Baltimore, United States
Duration: 23 Apr 201524 Apr 2015

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9501
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSatellite Data Compression, Communications, and Processing XI
Country/TerritoryUnited States
CityBaltimore
Period23/04/1524/04/15

Bibliographical note

Publisher Copyright:
© 2015 SPIE.

Keywords

  • Basis Pursuit
  • Hyperspectral Compression
  • Hyperspectral Imagery
  • Online Learning
  • Sparse Coding

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