# Towards Automatic Recognition of Wakes Generated by Dark Vessels in Sentinel-1 Images
###### Abstract  
The recognition of wakes generated by dark vessels is a tremendous and interesting challenge in the field of maritime surveillance by Synthetic Aperture Radar (SAR) images. The paper aims at assessing the detection performance in different scenarios by processing Sentinel-1 SAR images along with ground truth data. Results confirm that the Radon-based approach is an effective technique for wake-based detection of dark vessels, and they lead to a deeper understanding of the effects of different sea and wind conditions. In general, the best applicative scenario is a marine image characterized by homogeneous sea clutter; the presence of natural surface film or strong transition from low wind speed areas to more windy zones worsen the detection performance. Nonetheless, the proposed approach features dark vessel detection capabilities by identifying their wakes, without any a priori knowledge of their positions. dark vessels; radon transform; Sentinel-1; synthetic aperture radar images; wake detection +
Footnote †: journal: _Technical Note_  
**Volumes:** Number of 10  
Finally, a different approach exploiting the combination of the two cross-polarized channels has been proposed to improve the detection performance. However, the application of polarimetric technique is strongly limited by the availability and coverage of the relevant data [17]. Recent studies have detected ships using deep learning techniques, such as Convolutional Neural Networks (CNNs) for image-based feature extraction [18; 19; 20; 21; 22]. In the ambit of MDA, most studies using deep learning techniques focus on methods for the detection of artificial targets, such as ships, characterized by a strong backscattering. The above-mentioned techniques enable the detection of ships appearing as a cluster of bright pixels in SAR images. Then, if also the ship wakes are distinguishable, information about their route, i.e., heading and velocity, can be reached by wake detection and analysis [23; 24; 25; 26]. Currently, wake detection in SAR images has been faced following two main approaches. The first, more assessed, one exploits the typical appearance of wake in SAR images: since they appear as dark or bright lines, they can be detected as linear features in a noisy background, e.g., by Radon Transform [25; 26]. Recent studies [24] have proposed a different method for wake detection by using CNNs, using Inception v3 by Google as a representative model for detecting, classifying, and learning targets from images. Even if the algorithm has been applied only over 12 SAR images gathered by the TanDEM-X mission (including 13 wakes), the results suggests that the CNN is a useful tool for detecting the presence of wake on the wide SAR image. However, even though the automatic detection rate is quite high in general, data collected with higher wind condition (speed of about 7.9 m/s) shows an accuracy of wake detection of 60%. Both Radon-based and CNN-based techniques for wake detection strongly rely on a priori knowledge of vessel position; in other words, they have been developed to look for a wake in small image patch, e.g., for supporting the route estimation of previously detected vessels. In recent years, the operations of high-speed planing vessels for military, commercial and leisure use have increased. US navy has conducted a survey of the operators of high-speed craft [27], showing a high probability of serious injury. In fact, the use of this class of vessels has been signaled by pirates crossing from the mother ship to a passenger ship in the Somali area, or for trafficking goods in the Adriatic Sea, or for transporting drugs in the Alboran sea, or as a fishing boat plundering marine protected areas [28]. Since they have to face high-speed operations, these activities are typically carried out by using vessels, called GoFast [29; 30] with hulls made of fiberglass, a sharp, vertically rounded bow, and a transom stern, in order to reduce resistance.