# Preliminary Results of Ship Detection Technique by Wake Pattern Recognition in SAR Images
## 5 Conclusions  
The manuscript presents preliminary results of a technique that could be used for the detection of go-fast boats when their wakes are observable in SAR imagery in favorable weather conditions. The proposed algorithm implies the detection of pixels crossed by wake. To this end, a tile is selected around each pixel and the wake components are identified by using the Radon Transform. The wake structure is assumed to be composed of at least one dark and one bright linear component, which are detected in the Radon domain exploiting the relative angular distance set by hydrodynamic analysis. Then, the wake presence is validated by means of the wake merit index, which measures how bright and dark the detected components are with respect to the background of the tile. The above-mentioned steps allow the identification of a list of pixels crossed by the wake. In order to limit the false detection rate, an additional level of validation is introduced. In detail, since the wake extends for kilometers, neighbor pixels should be affected by its trace. Hence, the wakes validated by the merit index are plotted on the SAR image and the wakes not detected by neighbor pixels are classified as false detections.  
The proposed algorithm has been applied over an X-band image gathered by TerraSAR-X over the Gulf of Naples. Results show the potentialities of the proposed approach, in terms of wake pattern recognition in SAR images and rejection of false detections. In fact, two imaged wakes are correctly reconstructed, whereas the second level of validation discards the false detected wake. Additional investigations will be carried out to properly manage the missed detection, which is here due to very low value of merit index of narrow-V wake.  
The parameters influencing the wake detectability have been discussed from an operational user's perspective. Limitations due to adverse sea conditions exist, even if, thanks to the enhancing action of high-speed values, the probability of wake detection is larger than 70% for ship length of 20 m under moderate wind conditions (wind speed \(<\) 10 m/s).  
The proposed technique is presented as a stand-alone approach for ship detection in SAR images, but it can be conceived as integrated in a future hybrid system for MSA, that exploits together the potentialities of deep-learning and deterministic approaches, to optimize the computation time and the resources necessary to detect wakes. In a first step, the CNN could be used to automatically detect and delimit areas affected by the ship wakes in SAR images; these areas could be then analyzed in a subsequent step with deterministic approaches to confirm and exactly locate the wakes generated by go-fast boats.  
Future investigations will be devoted to assess the detection performance as a function of: (a) sea-wind conditions and ship velocity, and (b) evaluate the integration of the proposed approach in a hybrid system, in which the capabilities of the deterministic approach are exploited in delimited areas affected by the ship wakes in SAR images, previously identified by machine learning techniques.  
This research received no external funding.  
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The author declares no conflict of interest.