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. © 2014 Springer International Publishing.
Original language | English |
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Title of host publication | Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science |
Place of Publication | Switzerland |
Publisher | SpringerLink |
Pages | 63-78 |
Volume | 8695 |
ISBN (Print) | 9783319105833 |
DOIs | |
Publication status | Published - 2014 |
Event | 13th European Conference on Computer Vision - Zurich, Switzerland Duration: 6 Sep 2014 → 12 Sep 2014 Conference number: 13 |
Conference
Conference | 13th European Conference on Computer Vision |
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Abbreviated title | ECCV |
Country/Territory | Switzerland |
City | Zurich |
Period | 6/09/14 → 12/09/14 |