Low cost hyperspectral imaging using deep learning based spectral reconstruction

Tschannerl, Julius and Ren, Jinchang and Marshall, Stephen (2018) Low cost hyperspectral imaging using deep learning based spectral reconstruction. In: Hyperspectral Imaging Applications (HSI) 2018, 2018-10-10 - 2018-10-11.

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    Abstract

    The increasing number of applications of hyperspectral imaging results in a high demand for low cost, mobile devices. We propose a multispectral imaging (MSI) system based on time-multiplexed lighting using RGB Light Emitting Diodes (LED). We train a deep neural network that maps low dimensional multispectral input onto high dimensional hyperspectral (HSI) output that is collected with a HSI camera covering the range of 400 – 950 nm. Results on the 24 colour patches of the Macbeth colour checker chart show that with only five multispectral bands, a very accurate reconstruction of HSI data can be achieved.

    Author(s):Tschannerl, Julius ORCID logoORCID: https://orcid.org/0000-0002-4613-1693, Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194 and Marshall, Stephen ORCID logoORCID: https://orcid.org/0000-0001-7079-5628
    Item type:Conference or Workshop Item(Other)
    ID code:65968
    Keywords:hyperspectral imaging, deep learning, spectral reconstruction, LED illumination, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Strategic Research Themes > Measurement Science and Enabling Technologies
    Depositing user: Pure Administrator
    Date deposited:02 Nov 2018 15:04
    Last modified:15 Nov 2019 01:12
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/65968
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