Bayesian sparse representation for hyperspectral image super resolution

    Research output: Chapter in Book/Conference paperConference paper

    87 Citations (Scopus)

    Abstract

    © 2015 IEEE. Despite the proven efficacy of hyperspectral imaging in many computer vision tasks, its widespread use is hindered by its low spatial resolution, resulting from hardware limitations. We propose a hyperspectral image super resolution approach that fuses a high resolution image with the low resolution hyperspectral image using non-parametric Bayesian sparse representation. The proposed approach first infers probability distributions for the material spectra in the scene and their proportions. The distributions are then used to compute sparse codes of the high resolution image. To that end, we propose a generic Bayesian sparse coding strategy to be used with Bayesian dictionaries learned with the Beta process. We theoretically analyze the proposed strategy for its accurate performance. The computed codes are used with the estimated scene spectra to construct the super resolution hyperspectral image. Exhaustive experiments on two public databases of ground based hyperspectral images and a remotely sensed image show that the proposed approach outperforms the existing state of the art.
    Original languageEnglish
    Title of host publication2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    Place of PublicationUSA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages3631-3640
    ISBN (Print)9781467369640
    DOIs
    Publication statusPublished - 2015
    Event2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Boston, MA, USA, Boston, United States
    Duration: 7 Jun 201512 Jun 2015

    Conference

    Conference2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    CountryUnited States
    CityBoston
    Period7/06/1512/06/15

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  • Cite this

    Akhtar, N., Shafait, F., & Mian, A. (2015). Bayesian sparse representation for hyperspectral image super resolution. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3631-3640). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CVPR.2015.7298986