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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Bibliographic Details
Published in:Remote Sensing
Main Authors: Bianchi, Filippo Maria, Espeseth, Martine, Borch, Njål Trygve
Format: Article in Journal/Newspaper
Language:English
Published: MDPI 2020
Subjects:
VDP::Mathematics and natural science: 400::Physics: 430
VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430
Arctic
Online Access:https://hdl.handle.net/10037/18908
https://doi.org/10.3390/rs12142260
Description
Summary: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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