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.
Highlights
Due to the influence of short gravity waves and capillary waves on the sea surface, Bragg scattering of the sea surface is greatly weakened, causing the oil film to produce dark spots on synthetic aperture radar (SAR) images [1]
The encoder consists of the convolution layer, batch normalization layer, and rectified rectified linear unitand (ReLU), and itsisstructure similar the visual geometry group (VGG)-16 linear unit (ReLU), its structure similar toisthe visualtogeometry group (VGG)-16 network
The results show that FCN8s has a good overall segmentation effect
Summary
Due to the influence of short gravity waves and capillary waves on the sea surface, Bragg scattering of the sea surface is greatly weakened, causing the oil film to produce dark spots on synthetic aperture radar (SAR) images [1]. There are many studies on dark spot detection on SAR images of oil spill, among which the most widely used method is based on pixel grayscale threshold segmentation, such as a manual single threshold segmentation [3], an adaptive threshold segmentation method [4], and some double threshold segmentation methods [5]. Topouzelis et al proposed a fully connected feed forward neural network to monitor the dark spots in an oil spill area, and obtained a very high detection accuracy at that time [8]. Singha used artificial neural networks (ANN) to identify the characteristics of oil slicks and lookalikes [11] This method improved the segmentation accuracy to some extent and suppressed the influence of speckle noise on dark spot extraction, it still cannot obtain high segmentation accuracy and robustness. The conclusions and outlooks are discussed in the final section
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