Hierarchical beta process with gaussian process prior for hyperspectral image super resolution

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    Abstract

    © Springer International Publishing AG 2016.Hyperspectral cameras acquire precise spectral information, however, their resolution is very low due to hardware constraints.We propose an image fusion based hyperspectral super resolution approach that employes a Bayesian representation model. The proposed model accounts for spectral smoothness and spatial consistency of the representation by using Gaussian Processes and a spatial kernel in a hierarchical formulation of the Beta Process. The model is employed by our approach to first infer Gaussian Processes for the spectra present in the hyperspectral image. Then, it is used to estimate the activity level of the inferred processes in a sparse representation of a high resolution image of the same scene. Finally, we use the model to compute multiple sparse codes of the high resolution image, that are merged with the samples of the Gaussian Processes for an accurate estimate of the high resolution hyperspectral image. We perform experiments with remotely sensed and ground-based hyperspectral images to establish the effectiveness of our approach.
    Original languageEnglish
    Title of host publicationComputer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science
    EditorsB Leibe, J Matas, N Sebe, M Welling
    Place of PublicationSwitzerland
    PublisherSpringer
    Pages103-120
    Number of pages18
    Volume9907
    ISBN (Print)9783319464862
    DOIs
    Publication statusPublished - 2016
    Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
    Duration: 8 Oct 201616 Oct 2016

    Publication series

    NameLecture Notes in Computer Science

    Conference

    Conference14th European Conference on Computer Vision, ECCV 2016
    CountryNetherlands
    CityAmsterdam
    Period8/10/1616/10/16

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  • Cite this

    Akhtar, N., Shafait, F., & Mian, A. (2016). Hierarchical beta process with gaussian process prior for hyperspectral image super resolution. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science (Vol. 9907 , pp. 103-120). (Lecture Notes in Computer Science). Switzerland: Springer. https://doi.org/10.1007/978-3-319-46487-9_7