# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
## 1 Introduction  
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]. [PERSON] et al. pointed out that SAR oil spill detection includes three steps: (1) dark spot detection; (2) feature extraction; and (3) discrimination of oil slicks and lookalikes [2]. Among them, the accuracy of dark spot detection is bound to affect the extraction of oil spill location and area. 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, thus leading to an uneven intensity, high noise, or blurred boundaries of oil slicks or lookalikes, making the automatic segmentation of the oil spill area sometimes very difficult. Therefore, a robust and accurate segmentation method plays a crucial role in monitoring oil spills. 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]. Those methods have simple principles and fast implementation speeds, but they are easily affected by speckle noise and global gray unevenness, thus reducing the accuracy of dark spot recognition. The active contour models (ACM) are another common image segmentation method [6; 7]. Compared with traditional segmentation methods, the smooth and closed contours can be obtained by ACM. The most famous and widely used region-based ACM is the borderless ACM proposed by [PERSON] and [PERSON] [7]. The Chan-Vese model performs well in processingimages with weak edge and noise, but it cannot process images with uneven intensity and high speckle noise. With the popularization of neural networks and machine learning algorithms, some studies have used these methods for dark spot detection. [PERSON] 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]. [PERSON] et al. used a Weibull multiplication filter to suppress speckle noise, enhance the contrast between target and background, and used a multi-layer perceptron (MLP) neural network to segment the filtered SAR images [9]. [PERSON] et al. also proposed a new method to distinguish dark spots from the combination of the Weibull multiplication model (WMM) and pulse coupled neural networks (PCNN) [10]. [PERSON] used artificial neural networks (ANN) to identify the characteristics of oil slicks and lookalikes [11]. Although 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. [PERSON] et al. discussed the application of fuzzy c-means (FCM) clustering in SAR oil spill segmentation, in which it is easy to generate fragments in the segmentation process due to speckle noise images [12]. To suppress the influence of speckle noise on SAR image segmentation of an oil spill, [PERSON] et al. proposed a hierarchical clustering-based SAR image segmentation algorithm, which effectively maintained the shape characteristics of oil slicks in SAR images using the idea of multi-scale segmentation [13]. However, its ability to suppress speckle noise was not good, and the segmentation of weak boundary region was also not ideal. In recent years, deep learning methods have been successfully applied in extracting high level feature representations of images, especially in semantic segmentation. [PERSON] et al. changed the full connection layer of the traditional convolution neural networks (CNN) for pixel-based classification [14]. [PERSON] et al.