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Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution

  • Naveed Akhtar
  • Faisal Shafait
  • Ajmal Mian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8695)

Abstract

Existing hyperspectral imaging systems produce low spatial resolution images due to hardware constraints. We propose a sparse representation based approach for hyperspectral image super-resolution. The proposed approach first extracts distinct reflectance spectra of the scene from the available hyperspectral image. Then, the signal sparsity, non-negativity and the spatial structure in the scene are exploited to explain a high-spatial but low-spectral resolution image of the same scene in terms of the extracted spectra. This is done by learning a sparse code with an algorithm G-SOMP+. Finally, the learned sparse code is used with the extracted scene spectra to estimate the super-resolution hyperspectral image. Comparison of the proposed approach with the state-of-the-art methods on both ground-based and remotely-sensed public hyperspectral image databases shows that the presented method achieves the lowest error rate on all test images in the three datasets.

Keywords

Hyperspectral super-resolution spatio-spectral sparse representation 

Supplementary material

978-3-319-10584-0_5_MOESM1_ESM.zip (30.1 mb)
Electronic Supplementary Material (ZIP 30,829 KB)
978-3-319-10584-0_5_MOESM2_ESM.zip (0 kb)
Electronic Supplementary Material (ZIP 7 KB)

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Naveed Akhtar
    • 1
  • Faisal Shafait
    • 1
  • Ajmal Mian
    • 1
  1. 1.School of Computer Science and Software EngineeringThe University of Western AustraliaCrawleyUSA

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