Principal component analysis for compression of hyperspectral images

Research output: Contribution to conferencePaper

40 Citations (Scopus)

Abstract

In this paper, we explore the possibility to use the principal component analysis for compression of hyperspectral images. When the principal component analysis is applied to AVIRIS data that has 220 channels, we found that most energy is concentrated on a few eigenvalues, indicating that it may be possible to compress hyperspectral images significantly. The performance of the proposed algorithm is evaluated in terms of SNR and classification accuracies of selected classes. Experiments with AVIRIS data show promising results.

Original languageEnglish
Pages97-99
Number of pages3
Publication statusPublished - 2001
Event2001 International Geoscience and Remote Sensing Symposium (IGARSS 2001) - Sydney, NSW, Australia
Duration: 2001 Jul 92001 Jul 13

Other

Other2001 International Geoscience and Remote Sensing Symposium (IGARSS 2001)
CountryAustralia
CitySydney, NSW
Period01/7/901/7/13

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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    Lim, S., Sohn, K. H., & Lee, C. (2001). Principal component analysis for compression of hyperspectral images. 97-99. Paper presented at 2001 International Geoscience and Remote Sensing Symposium (IGARSS 2001), Sydney, NSW, Australia.