Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning
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,...
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| Published in: | Remote sensing (Basel, Switzerland) Vol. 12; no. 14; p. 2260 |
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| Language: | English |
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| Abstract | 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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| AbstractList | 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. |
| Author | Borch, Njål Bianchi, Filippo Maria Espeseth, Martine M. |
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| SubjectTerms | Classification Datasets Deep learning Design improvements Fysikk: 430 Image detection Image processing Image segmentation Matematikk og Naturvitenskap: 400 Mathematics and natural science: 400 Mineral oils Neural networks object detection Oil spills Physics: 430 Pollution detection Radar imaging Remote sensing SAR Semantics Sensors Synthetic aperture radar VDP Visualization |
| Title | Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning |
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