Journal of Spectral Imaging,   Volume 9   Article ID a16   (2020)

Peer reviewed Paper

Hyperspectral system trade-offs for illumination, hardware and analysis methods: a case study of seed mix ingredient discrimination

  • Carolina Blanch-Pérez del Notario  
  • Carlos López-Molina
  • Andy Lambrechts
  • Wouter Saeys
Imec, Kapeldreef 75, 3001, Leuven, Belgium
[email protected]
 Search for papers by this author
KU Leuven, Department of Biosystems, MeBioS, Kasteelpark Arenberg 30, 3001, Leuven, Belgium
[email protected]
 https://orcid.org/0000-0002-5849-4301
 Search for papers by this author
 Corresponding Author
Imec, Kapeldreef 75, 3001, Leuven, Belgium and KU Leuven, Department of Biosystems, MeBioS, Kasteelpark Arenberg 30, 3001, Leuven, Belgium
[email protected]
 Search for papers by this author

The discrimination power of a hyperspectral imaging system for image segmentation or object detection is determined by the illumination, the camera spatial–spectral resolution, and both the pre-processing and analysis methods used for image processing. In this study, we methodically reviewed the alternatives for each of those factors for a case study from the food industry to provide guidance in the construction and configuration of hyperspectral imaging systems in the visible near infrared range for food quality inspection. We investigated both halogen- and LED-based illuminations and considered cameras with different spatial–spectral resolution trade-offs. At the level of the data analysis, we evaluated the impact of binning, median filtering and bilateral filtering as pre- or post-processing and compared pixel-based classifiers with convolutional neural networks for a challenging application in the food industry, namely ingredient identification in a flour–seed mix. Starting from a basic configuration and by modifying the combination of system aspects we were able to increase the mean accuracy by at least 25 %. In addition, different trade-offs in performance-complexity were identified for different combinations of system parameters, allowing adaptation to diverse application requirements.

Keywords: system parameters, hyperspectral, illumination, pre- and post-processing, classification accuracy, convolutional neural networks, spatial–spectral resolution

Metrics

Downloads:

354

Full-Text HTML Views:

1

Abstract Views:

1,066