Please use this identifier to cite or link to this item:
Title: Determination of surface film thickness of heavy fuel oil using hyperspectral imaging and deep neural networks
Authors: Kieu, Hieu Trung
Law, Adrian Wing-Keung
Keywords: Engineering::Environmental engineering
Issue Date: 2022
Source: Kieu, H. T. & Law, A. W. (2022). Determination of surface film thickness of heavy fuel oil using hyperspectral imaging and deep neural networks. International Journal of Remote Sensing, 43(3), 997-1014.
Journal: International Journal of Remote Sensing
Abstract: The determination of surface oil film thickness is essential to safeguard the coastal water quality in major cities globally, particularly during the incidents of oil spills. The spilled oil film is typically very thin of the order of millimeters or less and thus the thickness quantification is very challenging. This study develops a laboratory approach for the thickness estimation using hyperspectral imaging combined with Deep Neural Networks for the image data analysis. Pool experiments were conducted in stagnant seawater with floating oil films of various thicknesses. Hyperspectral imaging was performed, and the images were augmented via a pixel extraction method. The data were then analyzed using two developed models of Dense Artificial Neural Network (DANN) and Convolutional Neural Network (CNN) to predict the thickness of the surface oil film. The results showed that both models managed to produce reasonably accurate predictions with a relatively high coefficient of determination of 0.87 and 0.95, respectively. Comparatively, the CNN model had overall better results by making use of the spatial information of surrounding pixels.
ISSN: 0143-1161
DOI: 10.1080/01431161.2022.2028200
Schools: School of Civil and Environmental Engineering 
Research Centres: Nanyang Environment and Water Research Institute 
Environmental Process Modelling Centre 
Rights: © 2022 Informa UK Limited, trading as Taylor & Francis Group. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

Citations 50

Updated on Sep 19, 2023

Web of ScienceTM
Citations 50

Updated on Sep 21, 2023

Page view(s)

Updated on Sep 26, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.