Home // SIGNAL 2018, The Third International Conference on Advances in Signal, Image and Video Processing // View article
Authors:
Mohammadmehdi Saberioon
Petr Císař
Pavel Souček
Laurent Labbé
Pablo Pelissier
Keywords: Pre-processing algorithms; classification; Support vector machine;Fish diet
Abstract:
The main aim of this study was to compare the performance of different pre-processing algorithms when coupled with Support vector machine as the classifier to differentiate live fish based on their diet received during cultivation using hyperspectral imagery system. Rainbow trout (Oncorhynchus mykiss) were fed either a fish meal-based diet or a 100 % plant-based diet. Hyperspectral images were made using a push-broom hyperspectral imaging system in the spectral region of 394-1009 nm. Six spectral pre-treatment methods were used, including Savitzky-Golay, First Derivative, Second Derivative, Standard Normal Variate and Multiplicative Scatter Correction were employed to improve the robustness and performance of the classifier. According to the criteria of correct classification rate and Kappa coefficient, the support vector machine with linear kernel when coupled with Savitzky-Golay pre-processing was determined as the best method for classifying live fish due to their diet.
Pages: 21 to 24
Copyright: Copyright (c) IARIA, 2018
Publication date: May 20, 2018
Published in: conference
ISSN: 2519-8432
ISBN: 978-1-61208-638-5
Location: Nice, France
Dates: from May 20, 2018 to May 24, 2018