This is a typical problem where machine-learning techniques can be very effective. In addition to the difficulties to extract wakes from a complex sea scene, speckle and existence of different types and aspects of wakes in different weather and sea conditions and in different SAR modes and resolutions make the automatic wake detection task even harder. Highly twisting routes and irregular sea features like low wind areas and natural slicks are just few examples of how the task of wake detection, which is well accomplished by photo‑interpretation, can be actually a challenging task for fully automatic algorithms. In any case, it is also very expensive to run it on whole images or very large clips. Such approach suffers from three issues: - It assumes rectilinear wakes - Speckle and the non‑uniform appearance of wakes can decrease their detectability - In case of wake detection without previous ship detection, the choice of the size of image clip to process is critical, because the Radon assumes segments limited only by the clip itself: too large clips, therefore, lead to missed detections, whilst clips too small lead to many false alarms. Historically this problem has been addressed by looking for peaks in the Radon transform of the SAR image. But automatic algorithms have always shown their limits. When visible, wakes are quite evident and their detection by mean of photo‑interpretation is a quite easy task even when they are not straight lines or have irregular shapes. The importance of wake detection on SAR images is twofold: to estimate vessel velocity by exploiting the Doppler anomaly (a ship and its wake show different Doppler frequency due to the different speed) and to detect ships with faint scattering (very small boats, or small and very fast boats).