# Preliminary Results of Ship Detection Technique by Wake Pattern Recognition in SAR Images
###### Abstract
Both transverse waves and divergent waves mostly develop in a cone which, if the ship is approximated by a point disturbance [19], has an aperture of 38\({}^{\circ}\)56\({}^{\prime}\), defined by the cusp waves generated along the crest of the Kelvin arms. A diagram of the wake structure is shown in [16; 20]. The wake observability in SAR image is strongly related to ship-, SAR- and sea-related conditions. In fact, it is more distinguishable when: (a) the ship is larger, (b) the ship is faster, (c) the radar incidence angle is smaller, and (d) the ship is moving along the looking direction of the radar [21]. In addition, the wake features are more detectable when wind speed is in the range of 3-7 m/s, even if the visibility of the Kelvin arms is improved for lower wind speeds, since the contrast between cusp waves and background sea surface roughness is higher. Since wakes appear as dark or bright lines, the existing wake detection algorithms identify linear features in a noisy background. In [22], the authors review the existing algorithms for line detection in SAR images, considering the most used approaches and confirm the large utilization of the Radon transform for wake detection algorithms. The Radon transform [16] integrates image brightness along straight lines. Therefore, one bright (dark) linear feature in the input image leads to one peak (trough) in the Radon domain. Radon transform was first proposed for ship wake detection in SAR images by [PERSON] [23] in 1986 and since then, plenty of experimental results have analyzed its limitations and confirmed its effectiveness for wake detection purposes. In this ambit, an original wake detection algorithm for ship velocity estimation is presented in [24]. The technique takes advantages from the Radon-based previous techniques [16; 25], but it has been conceived to overcome their intrinsic limitations by exploiting the relative angular distance of wake components during the detection process. The results [24; 26; 27] showed that the vast majority of wake components are correctly detected and also validated in critical situations. The technique in [24] has been recently improved in [28; 29] adding a pre-processing step enhancing the wake components in the Radon domain. Results confirm the high accuracy (larger than 80%) of detection performance of the Radon-based technique. However, the above-mentioned approaches start from an a-priori knowledge of the ship location (detected thanks to ship echoes in the image) and, hence, are not applicable to go-fast boats. Recent studies have detected ships using deep learning techniques such as Convolutional Neural Networks (CNNs) for image-based feature extraction [30; 31; 32; 33]. Interesting results of CNN-based wake detection are shown in [34]. Nonetheless the limited number of available SAR images, the authors [34] state that the CNN is a useful tool for detecting the presence of wake. However, the purpose of the paper is the estimation of ship velocity, and the technique has been applied over a small tile including ship echo. Hence, neither deterministic nor machine learning based techniques are currently available for the detection of the go-fast boats. Then, the manuscript presents preliminary results of a technique for wake detection in SAR images, without any a priori knowledge of ship position and enabling the detection of go-fast boats. The paper is organized as follows: the proposed method is detailed in Section 2 and results are shown in Section 3 and discussed in Section 4. Finally, conclusions summarize the potentialities of the approach and future developments.