RT journal article
T1 Oil Spill Classification Using an Autoencoder and Hyperspectral Technology
A1 Carrasco García, María Gema
A1 Rodríguez García, María Inmaculada
A1 Ruíz Aguilar, Juan Jesús
A1 Deka, Lipika
A1 Elizondo, David
A1 Turias Domínguez, Ignacio José
A2 Ingeniería Industrial e Ingeniería Civil
A2 Ingeniería Informática
K1 hyperspectral
K1 artificial neural network
K1 autoencoder
K1 decision tree
K1 oil spills
K1 machine learning
K1 classification
AB Hyperspectral technology has been playing a leading role in monitoring oil spills in marine environments, which is an issue of international concern. In the case of monitoring oil spills in local areas, hyperspectral technology of small dimensions is the ideal solution. This research explores the use of encoded hyperspectral signatures to develop automated classifiers capable of discriminating between polluted and clean water and distinguishing between various types of oil. The overall objective is to leverage these classifiers to be able to improve the performance of conventional systems that rely solely on hyperspectral imagery. The acquisition of the hyperspectral signatures of water and hydrocarbons was carried out with a spectroradiometer. The range of the spectroradiometer used in this study covers the ranges between [350–1000] (visible near-infrared) and [1000–2500] (short-wavelength infrared). This gives detailed information regarding the targets of interest. Different neural autoencoders (AEs) have been developed to reduce inputs into different dimensions, from 1 to 15. Each of these encoded sets was used to train decision tree (DT) classifiers. The results are very promising, as they show that the AE models encoded data with correlation coefficients above 0.95. The classifiers trained with the different sets provide accuracies close to 1.
PB MDPI
SN 2077-1312
YR 2024
FD 2024
LK http://hdl.handle.net/10498/33703
UL http://hdl.handle.net/10498/33703
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 08-nov-2026