Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging
Author:
Zhan Ce123, Bai Kai123, Tu Binrui123, Zhang Wanxing123
Affiliation:
1. Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Jingzhou 430023, China 2. Xi’an Key Laboratory of Tight Oil (Shale Oil) Development, Xi’an Shiyou University, Xi’an 710065, China 3. School of Computer Science, Yangtze University, Jingzhou 430023, China
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.
Funder
Open Fund of Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University Open Fund of Xi’an Key Laboratory of Tight oil Development of the Scientific Research Projects of the Hubei Provincial Department of Education
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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