A novel deep learning method for marine oil spill detection from satellite synthetic aperture radar imagery

Oil spill discharges from operational maritime activities like ships, oil rigs and other structures, leaking pipelines, as well as natural hydrocarbon seepage pose serious threats to marine ecosystems and fisheries. Satellite synthetic aperture radar (SAR) is a unique microwave instrument for marine...

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Veröffentlicht in:	Marine pollution bulletin 2022-06, Vol.179, p.113666-113666, Article 113666
Hauptverfasser:	Huang, Xudong, Zhang, Biao, Perrie, William, Lu, Yingcheng, Wang, Chen
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Convolutional neural network Datasets Deep learning Detection Drilling rigs Environmental conditions Faster R-CNN Fisheries Incidence angle Machine learning Marine ecosystems Marine fish Methods Neural networks Oil spill Oil spills Petroleum pipelines Pipelines Pollution detection Radar Radar imagery Radar imaging Radarsat SAR (radar) Satellites Seepage Ships Submarine pipelines Synthetic aperture radar Vertical polarization Wind speed Workstations
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