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Spectral Super-resolution for RGB Images using Class-based BP Neural Networks

Han, X; Yu, J; Xue, JH; Sun, W; (2019) Spectral Super-resolution for RGB Images using Class-based BP Neural Networks. In: (Proceedings) 2018 Digital Image Computing: Techniques and Applications (DICTA). IEEE Green open access

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Abstract

Hyperspectral images are of high spectral resolution and have been widely used in many applications, but the imaging process to achieve high spectral resolution is at the expense of spatial resolution. This paper aims to construct a high-spatial-resolution hyperspectral (HHS) image from a high-spatial-resolution RGB image, by proposing a novel class-based spectral super-resolution method. With the help of a set of RGB and HHS image-pairs, our proposed method learns nonlinear spectral mappings between RGB and HHS image-pairs using class-based back propagation neural networks (BPNNs). In the training stage, unsupervised clustering is used to divide an RGB image into several classes according to spectral correlation, and the spectrum-pairs from the classified RGB images and the corresponding HHS images are used to train the BPNNs, to establish the nonlinear spectral mapping for each class. In the spectral super-resolution stage, a supervised classification is used to classify the given RGB image into the classes determined during the training stage, and the final HHS image is reconstructed from the classified given RGB image using the trained BPNNs. Comparisons on three standard datasets, ICVL, CAVE and NUS, demonstrate that, our proposed method achieves a better spectral super-resolution quality than related state-of-the-art methods.

Type: Proceedings paper
Title: Spectral Super-resolution for RGB Images using Class-based BP Neural Networks
Event: 2018 Digital Image Computing: Techniques and Applications (DICTA)
ISBN-13: 9781538666029
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/DICTA.2018.8615862
Publisher version: https://doi.org/10.1109/DICTA.2018.8615862
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Training , Spatial resolution , Hyperspectral imaging , Neural networks , Image reconstruction , Reflectivity
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/10070886
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