RT journal article
T1 Hyperspectral imaging and artificial neural networks for oil spills automatic detection
A1 Carrasco García, María Gema
A1 Rodríguez García, María Inmaculada
A1 González Enrique, Francisco Javier
A1 Ruiz Águilar, Juan Jesús
A1 Alcántara Pérez, José Manuel
A1 Turias Domínguez, Ignacio José
A2 Ingeniería Industrial e Ingeniería Civil
A2 Ingeniería Informática
K1 oil spills
K1 hyperspectral
K1 machine learning
K1 autoencoder
K1 HSI· remote sensing
K1 ANNs
AB The detection of hydrocarbon spills in marine environments is a critical challenge due to their severe impact on aquatic ecosystems. Hyperspectral imaging technology has emerged as a promising tool for detecting such spills, offering high-resolution spectral data for accurate identification. This work aims to develop an automated procedure for detecting hydrocarbon spills in local marine areas using a compact hyperspectral camera installed on a drone. The methodology integrates machine learning with hyperspectral technology, employing hyperspectral signatures of various hydrocarbons and water to create a spectral knowledge base for classification. A novel approach is used in the classifier training, where hyperspectral signatures are directly utilized as input to an artificial neural network, bypassing the need for extensive image labelling, eliminating the requirement for a large image dataset, and significantly reducing the computational costs associated with deep learning models. The trained classifiers, using autoencoders for encoding hyperspectral signatures, were applied to classify hyperspectral profiles (hyperpixels) in hyperspectral images. The results were highly promising, with the classifiers achieving near-perfect accuracy in detecting contaminated water and distinguishing between different hydrocarbons. Recall and F-score values above 0.90 demonstrate the effectiveness of the proposed approach, offering an efficient, scalable solution for real-time monitoring of marine spills.
PB Oxford University Press
SN 1368-9894
YR 2026
FD 2026-02
LK http://hdl.handle.net/10498/39529
UL http://hdl.handle.net/10498/39529
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 08-nov-2026