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. © 2014 Springer International Publishing.
CITATION STYLE
Akhtar, N., Shafait, F., & Mian, A. (2014). Sparse spatio-spectral representation for hyperspectral image super-resolution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8695 LNCS, pp. 63–78). Springer Verlag. https://doi.org/10.1007/978-3-319-10584-0_5
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