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Resolution enhancement of hyperspectral images using a learning-based super-resolution mapping technique

  • School of Electronics and Information Engineering, Harbin Institute of Technology

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

Abstract

A fast and efficient spatial-spectral fusion method for resolution enhancement of hyperspectral imagery is proposed in this paper. A linear mixture model and fully constrained least squares based unmixing algorithm are applied for spectral unmixing of the hyperspectral imagery and the resulted fractional images are processed using a spatial-spectral information correlation model through a learning-based super-resolution mapping technique. To validate the performance of the method, experiments are carried out on real images. The obtained results validate the reliability of the technique. The main advantages of the proposed method include its autonomous nature so that it doesn't need any high resolution secondary source of data, its acceptable performance, and its low computational cost which makes it favorable for real-time target recognition and tracking applications.

Original languageEnglish
Title of host publication2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages813-816
Number of pages4
ISBN (Print)9781424433957
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Cape Town, South Africa
Duration: 12 Jul 200917 Jul 2009

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume3

Conference

Conference2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009
Country/TerritorySouth Africa
CityCape Town
Period12/07/0917/07/09

Keywords

  • Fractional image
  • Hyperspectral imagery
  • Resolution enhancement
  • Spectral unmixing
  • Super-resolution mapping

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