Journal
REMOTE SENSING
Volume 12, Issue 14, Pages -Publisher
MDPI
DOI: 10.3390/rs12142260
Keywords
oil spills; deep learning; SAR; object detection; image segmentation
Categories
Funding
- CIRFA through the Research Council of Norway (Norges forskningsrad) [237906]
- KSAT (Kongsberg Satellite Services) within the PETROMAKS 2 program of the Research Council of Norway (Norges forskningsrad) [282082]
- KSAT
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We propose a deep-learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. Through a carefully designed neural network model for image segmentation trained on an extensive dataset, we obtain state-of-the-art performance in oil spill detection, achieving results that are comparable to results produced by human operators. We also introduce a classification task, which is novel in the context of oil spill detection in SAR. Specifically, after being detected, each oil spill is also classified according to different categories of its shape and texture characteristics. The classification results provide valuable insights for improving the design of services for oil spill monitoring by world-leading providers. Finally, we present our operational pipeline and a visualization tool for large-scale data, which allows detection and analysis of the historical occurrence of oil spills worldwide.
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