Dark Spot Detection in SAR Images of Oil Spill Using Segnet

Clicks: 233
ID: 13983
2018
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Abstract
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.
Reference Key
guo2018darkapplied Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Guo, Hao;Wei, Guo;An, Jubai;
Journal applied sciences
Year 2018
DOI
DOI not found
URL
https://www.mdpi.com/2076-3417/8/12/2670
Keywords Keywords not found

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