Open Access
Open access
Sensors, volume 24, issue 2, pages 411

Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging

Publication typeJournal Article
Publication date2024-01-10
Journal: Sensors
scimago Q1
SJR0.786
CiteScore7.3
Impact factor3.4
ISSN14243210, 14248220
Biochemistry
Analytical Chemistry
Atomic and Molecular Physics, and Optics
Electrical and Electronic Engineering
Instrumentation
Abstract

Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models—DRSNet, CNN-Visual Transformer, and GCN—conducting a comprehensive analysis to evaluate the advantages and limitations of each model.

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