Dimensionality reduction of hyperspectral images for color display using segmented independent component analysis

Zhu Yingxuan, Pramod K. Varshney, Chen Hao

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

2 Scopus citations

Abstract

The problem of dimensionality reduction for color representation of hyperspectral images has received recent attention. In this paper, several independent component analysis (ICA) based approaches are proposed to reduce the dimensionality of hyperspectral images for visualization. We also develop a simple but effective method, based on correlation coefficient and mutual information (CCMI), to select the suitable independent components for RGB color representation. Experimental results are presented to illustrate the performance of our approaches.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings
PublisherIEEE Computer Society
PagesIV97-IV100
ISBN (Print)1424414377, 9781424414376
DOIs
StatePublished - Jan 1 2007
Event14th IEEE International Conference on Image Processing, ICIP 2007 - San Antonio, TX, United States
Duration: Sep 16 2007Sep 19 2007

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume4
ISSN (Print)1522-4880

Other

Other14th IEEE International Conference on Image Processing, ICIP 2007
CountryUnited States
CitySan Antonio, TX
Period9/16/079/19/07

Keywords

  • Dimensionality reduction
  • Hyperspectral imaging
  • ICA
  • Visualization

ASJC Scopus subject areas

  • Engineering(all)

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    Yingxuan, Z., Varshney, P. K., & Hao, C. (2007). Dimensionality reduction of hyperspectral images for color display using segmented independent component analysis. In 2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings (pp. IV97-IV100). [4379963] (Proceedings - International Conference on Image Processing, ICIP; Vol. 4). IEEE Computer Society. https://doi.org/10.1109/ICIP.2007.4379963