Detection of Oil Spills in Simulated Coastal Marine Environments via Hyperspectral Imaging and 3-D CNN

Abstract

The early detection and classification of oil spills in the ocean is crucial to mitigate potential harm to the environment and economy. Optical hyperspectral remote sensing is an effective method for monitoring and tracking these events, primarily because of the high spectral precision and the possibility of multiscale spatial coverage. Despite the great potential of these sensors, the detection of ocean oil spills through optical images, especially in coastal environments, still requires human interpretation, delaying response efforts. Accurately measuring oil spill thickness is crucial for understanding the incident and its impacts. However, it remains a challenge due to limited studies, scarce data, and unreliable measurement techniques. A promising approach to automating hyperspectral imaging processing is utilizing deep learning models. This research evaluates the effectiveness of 3-D convolutional neural networks in detecting and classifying oil spill thickness through hyperspectral imaging in the visible/near-infrared and short-wave infrared regions. The data used to train the model were acquired through a controlled field experiment in which oil spills were simulated under conditions similar to marine coastal environments. Savitzky–Golay filters and continuous wavelet signal decomposition were applied in data preprocessing before the application of a 3-D convolutional neural network. The combination of continuous wavelet analysis and 3-D convolutional neural networks proved to be a promising approach to identify and estimate oil spill thickness on hyperspectral data, achieving an overall accuracy of 97.85%, a Kappa coefficient of 0.96, and a weighted F1-score of 0.98. This provides a scalable and accurate approach for early response to oil spill monitoring in coastal environments.