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Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution*Naveed Akhtar, Faisal Shafait, and Ajmal Mian School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, 6009, Crawley, WA, USAnaveed.akhtar@research.uwa.edu.au faisal.shafait@uwa.edu.au ajmal.mian@uwa.edu.au 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 Electronic Supplementary Material:
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LNCS 8695, p. 63 ff.
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© Springer International Publishing Switzerland 2014