Dark Spot Detection in SAR Images of Oil Spill Using Segnet

Damping Bragg scattering from the ocean surface is the basic underlying principle of synthetic aperture radar (SAR) oil slick detection, and they produce dark spots on SAR images. Dark spot detection is the first step in oil spill detection, which affects the accuracy of oil spill detection. However...

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Bibliographic Details
Published in:Applied sciences 2018-12, Vol.8 (12), p.2670
Main Authors: Guo, Hao, Wei, Guo, An, Jubai
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Backscattering
Chemical analysis
Classification
Clustering
Deep learning
Gravitational waves
Human factors
Image detection
Image processing
image segmentation
International conferences
Neural networks
Noise
Noise intensity
Ocean currents
Oil slicks
Oil spills
Pixels
Seawater
segnet
Semantics
Synthetic aperture radar
synthetic aperture radar (SAR)
Teaching methods
Unmanned aerial vehicles
Water analysis
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Summary:Damping Bragg scattering from the ocean surface is the basic underlying principle of synthetic aperture radar (SAR) oil slick detection, and they produce dark spots on SAR images. Dark spot detection is the first step in oil spill detection, which affects the accuracy of oil spill detection. However, some natural phenomena (such as waves, ocean currents, and low wind belts, as well as human factors) may change the backscatter intensity on the surface of the sea, resulting in uneven intensity, high noise, and blurred boundaries of oil slicks or lookalikes. In this paper, Segnet is used as a semantic segmentation model to detect dark spots in oil spill areas. The proposed method is applied to a data set of 4200 from five original SAR images of an oil spill. The effectiveness of the method is demonstrated through the comparison with fully convolutional networks (FCN), an initiator of semantic segmentation models, and some other segmentation methods. It is here observed that the proposed method can not only accurately identify the dark spots in SAR images, but also show a higher robustness under high noise and fuzzy boundary conditions.
ISSN:2076-3417
2076-3417
DOI:10.3390/app8122670