Journal of Spectral Imaging,   Volume 9   Article ID a19   (2020)

Peer reviewed Tutorial

Deep learning classifiers for near infrared spectral imaging: a tutorial

  • Jun-Li Xu  
  • Cecilia Riccioli
  • Ana Herrero-Langreo
  • Aoife A. Gowen
Faculty of Agriculture and Forestry Engineering, Department of Animal Production, University of Cordoba, Cordoba, Spain

 https://orcid.org/0000-0002-0998-7150
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UCD School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield, Dublin 4, Ireland

 https://orcid.org/0000-0003-3258-6248
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UCD School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield, Dublin 4, Ireland

 https://orcid.org/0000-0002-9494-2204
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 Corresponding Author
UCD School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield, Dublin 4, Ireland
[email protected]
 https://orcid.org/0000-0002-4442-7538
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Deep learning (DL) has recently achieved considerable successes in a wide range of applications, such as speech recognition, machine translation and visual recognition. This tutorial provides guidelines and useful strategies to apply DL techniques to address pixel-wise classification of spectral images. A one-dimensional convolutional neural network (1-D CNN) is used to extract features from the spectral domain, which are subsequently used for classification. In contrast to conventional classification methods for spectral images that examine primarily the spectral context, a three-dimensional (3-D) CNN is applied to simultaneously extract spatial and spectral features to enhance classification accuracy. This tutorial paper explains, in a stepwise manner, how to develop 1-D CNN and 3-D CNN models to discriminate spectral imaging data in a food authenticity context. The example image data provided consists of three varieties of puffed cereals imaged in the NIR range (943–1643 nm). The tutorial is presented in the MATLAB environment and scripts and dataset used are provided. Starting from spectral image pre-processing (background removal and spectral pre-treatment), the typical steps encountered in development of CNN models are presented. The example dataset provided demonstrates that deep learning approaches can increase classification accuracy compared to conventional approaches, increasing the accuracy of the model tested on an independent image from 92.33 % using partial least squares-discriminant analysis to 99.4 % using 3-CNN model at pixel level. The paper concludes with a discussion on the challenges and suggestions in the application of DL techniques for spectral image classification.

Keywords: spectral imaging, deep learning, near infrared, classification, convolutional neural network

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