Sparse spatio-spectral representation for hyperspectral image super-resolution

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    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 languageEnglish
    Title of host publicationComputer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science
    Place of PublicationSwitzerland
    PublisherSpringerLink
    Pages63-78
    Volume8695
    ISBN (Print)9783319105833
    DOIs
    Publication statusPublished - 2014
    Event13th European Conference on Computer Vision - Zurich, Switzerland
    Duration: 6 Sep 201412 Sep 2014
    Conference number: 13

    Conference

    Conference13th European Conference on Computer Vision
    Abbreviated titleECCV
    CountrySwitzerland
    CityZurich
    Period6/09/1412/09/14

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