Reconstruction of hyperspectral spectra of fish fillets using multi-wavelength imaging and point spectroscopy

John Chauvin; Fartash Vasefi; Kouhyar Tavakolian; Alireza Akhbardeh; Nicholas MacKinnon; Jianwei Qin; Diane E. Chan; Moon S. Kim

doi:10.1117/12.2559230

19 May 2020 Reconstruction of hyperspectral spectra of fish fillets using multi-wavelength imaging and point spectroscopy

John Chauvin, Fartash Vasefi, Kouhyar Tavakolian, Alireza Akhbardeh, Nicholas MacKinnon, Jianwei Qin, Diane E. Chan, Moon S. Kim

Author Affiliations +

Proceedings Volume 11421, Sensing for Agriculture and Food Quality and Safety XII; 114210I (2020) https://doi.org/10.1117/12.2559230
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Our goal is to develop a reliable and cost-effective spectral imaging system with sparse spectral measurements. Relative to standard RGB imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of LEDs to collect images in multiple wavelengths. We would like to overcome these limitations by employing a novel spectral reconstruction algorithm to recreate the full-resolution reflectance or fluorescence spectrum from an optimized selection of images at a sparse set of wavelengths. This algorithm is aided by a single full-resolution spectrometer measurement representing an average value over the selected spatial scene. We use a genetic algorithm-based methodology to identify the optimal wavelengths for sparse spectral measurement and invoke a cost function that includes a weight vector to emphasize minimization of errors in key portions of the spectrum. To validate the proposed algorithm, reflectance spectra in the visible and NIR (400-1000 nm) and fluorescence spectra with UV illumination were collected from fish fillets to validate our methods. In this paper, we discuss the reconstruction algorithm and the genetic algorithm-based optimization method that we use to determine the optimal set of wavelengths for imagery collection. We also present results from a fish species classification study using the reconstructed spectra as feature sets for four common machine learning algorithms. The classification accuracies based on these reconstructed spectra are on par with the accuracies that result from using the original full spectral resolution data.

Conference Presentation

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John Chauvin, Fartash Vasefi, Kouhyar Tavakolian, Alireza Akhbardeh, Nicholas MacKinnon, Jianwei Qin, Diane E. Chan, and Moon S. Kim "Reconstruction of hyperspectral spectra of fish fillets using multi-wavelength imaging and point spectroscopy", Proc. SPIE 11421, Sensing for Agriculture and Food Quality and Safety XII, 114210I (19 May 2020); https://doi.org/10.1117/12.2559230

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