Optimizing signal and discriminant information for hyperspectral images

S. Youn, C. Lee

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

Abstract

Most compression methods have been developed to minimize mean squared errors. On the other hand, discriminant information required to distinguish between various classes is also vital for applications where hyperspectral images are used for classification. However, the discriminant information may not be well preserved during lossy compression process since it may not be large in energy. To deal with this problem, a hybrid compression method was proposed. In this paper, we propose a method to optimize signal and discriminant information for the hybrid method. Experimental results show some improvement can be achieved by this optimization.

Original languageEnglish
Title of host publicationIISA 2016 - 7th International Conference on Information, Intelligence, Systems and Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509034291
DOIs
Publication statusPublished - 2016 Dec 14
Event7th International Conference on Information, Intelligence, Systems and Applications, IISA 2016 - Chalkidiki, Greece
Duration: 2016 Jul 132016 Jul 15

Publication series

NameIISA 2016 - 7th International Conference on Information, Intelligence, Systems and Applications

Other

Other7th International Conference on Information, Intelligence, Systems and Applications, IISA 2016
CountryGreece
CityChalkidiki
Period16/7/1316/7/15

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All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Artificial Intelligence
  • Social Sciences (miscellaneous)

Cite this

Youn, S., & Lee, C. (2016). Optimizing signal and discriminant information for hyperspectral images. In IISA 2016 - 7th International Conference on Information, Intelligence, Systems and Applications [7785404] (IISA 2016 - 7th International Conference on Information, Intelligence, Systems and Applications). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IISA.2016.7785404