Hyperspectral Marine Oil Spill Monitoring Using a Dual-Branch Spatial–Spectral Fusion Model

Abstract

Marine oil spills pose a crucial concern in the monitoring of marine environments, and optical remote sensing serves as a vital means for marine oil spill detection. However, optical remote sensing imagery is susceptible to interference from sunglints and shadows, leading to diminished spectral differences between oil films and seawater. This makes it challenging to accurately extract the boundaries of oil–water interfaces. To address these aforementioned issues, this paper proposes a model based on the graph convolutional architecture and spatial–spectral information fusion for the oil spill detection of real oil spill incidents. The model is experimentally evaluated using both spaceborne and airborne hyperspectral oil spill images. Research findings demonstrate the superior oil spill detection accuracy of the developed model when compared to Graph Convolutional Network (GCN) and CNN-Enhanced Graph Convolutional Network (CEGCN), across two hyperspectral datasets collected from the Bohai Sea. Moreover, the performance of the developed model in oil spill detection remains optimal, even with only 1% of the training samples. Similar conclusions are drawn from the oil spill hyperspectral data collected from the Yellow Sea. These results validate the efficacy and robustness of the proposed model for marine oil spill detection.

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

“…JunFang Yang et al introduced an oil spill detection model rooted in graph convolution architecture and spatial–spectral information. Through a comparative analysis with the GCN and CEGCN models, this approach demonstrated a notable enhancement in detection accuracy [ 15 ]. Seyd Teymoor Seydi et al utilized a one-dimensional multiscale residual convolutional neural network to classify pixels based on the spectral features of oil spill region pixels, aiming to achieve the detection of oil spill areas [ 16 ].…”