###### Abstract
When the coastal environment is harsh, the intensities of some water regions in a high sea state are higher than the intensities of the land regions, and the intensities of mud and sand flats in intertidal zones are lower than the intensities of the calm sea regions. Wrong segmentation will then occur in both sea and land regions. To avoid the wrong segmentation caused by the intensity intersection of different regions, [PERSON] et al. [16] proposed a classification scheme of mud and sand flats on intertidal flats using different polarimetric decomposition components. [PERSON] et al. [17] proposed a coastline extraction scheme in harsh coastal environments using a global constant false alarm rate (CFAR) thresholding segmentation of a normalized cross-section in different polarimetric channels. However, it is still difficult to segment the land and water along ports using the existing polarimetric methods since the port water regions are interfered with by surrounding strong scattering buildings and ships. Figure 1: Structure diagram of a port, where the boxes drawn in orange denote buildings, and the red boxes denote ships. Based on the accurate sea-land segmentation, the geometric features of the contour of ports can then be extracted and recognized. From the point of view that the feature points of the outline of the port area are denser than those of non-port areas, [PERSON] et al. [2] proposed a harbor detection method based on feature point merging. Starting from the assumption that the two sides of jetties and breakwaters are parallel, [PERSON] et al. [3] proposed a harbor detection method based on the characteristics of parallel curves. Starting from the assumption that the overall structural characteristics of the port contour are in order, [PERSON] et al. [4] detected the port by extracting corners on the contour using wavelet transform and described the corners using chain code descriptors. [PERSON] et al. [5] detected a harbor by a combination of edge detection and scale-invariant feature transform keypoint extraction. According to the assumption that the contours of jetties were concave and convex, [PERSON] et al. [6] first proposed a method based on jetty scanning and merging. [PERSON] et al. [7] improved the scanning method from two directions to four directions. [PERSON] et al. [8] extended the method from four directions to multiple directions. However, the problem of the above geometric-feature-based methods is that the geometric features of ports are unstable and blur in SAR images due to the strong coherent speckle, the variety of port morphology, and the berthed ships on the port. Accurate coastline detection and sophisticated feature extraction methods are needed to detect and measure blurry geometric features. This directly leads to a decrease in accuracy and an increase in computational time. When the sea condition is rough and the interference is strong, wrong segmentation and incorrect feature extraction will make the port undetectable. When the coastal outline is complex, the natural protruding terrain along the coast will form false alarms. In recent years, deep learning neural network methods, including the Region-based Convolutional Neural Network (R-CNN) [18], the Fast/Faster R-CNN [19], and the Mask R-CNN [20], are widely used in all kinds of object detection. However, it is difficult to collect sufficient training samples to perform port detection in SAR images because of the varied morphology of ports and the strong coherent speckle. Compared with port detection in optical remote sensing images, ports in synthetic aperture radar (SAR) images not only have salient geometric features but also have a significant scattering intensity distribution feature formed by the surrounding buildings and ships. The extraction of a strong scattering region can provide effective information for port detection in SAR images.