## 1 Introduction
It is well known that the incidence angle between the radar beam and the surface influences the amount of signal that is backscattered to the sensor. In this regard, steep incidence angles lead to a higher backscatter intensity, particularly from the sea, which reduces the contrast between the ship and its surroundings. On the contrary, shallow incidence angles benefit from a reduced return from the sea surface, resulting in a higher image contrast, but the occurrence of azimuth and range ambiguities increases [16]. Secondly, natural phenomena such as ocean currents, waves, wind, or other meteorological conditions can also impact the backscattering properties of the sea surface, leading to a reduction of image contrast and limiting the application of automatic ship detection methods [16]. This decrease in algorithm performance causes inevitably the appearance of either false positives (i.e., wrong detections) or miss-detections (i.e., undetected ships). Additionally, another important issue to consider is that vessels might be moving during the acquisition of the SAR image. Since SAR systems depend on the coherent processing of transmitted and received electromagnetic signals over time, vessel movement inevitably causes blurriness and defocusing effects, further complicating automatic detection. Reducing the impact of target movement requires specific processing to be applied before any detection technique. Different methods aim at reducing these undesired effects caused by the target's motion, including Phase Gradient Autofocus algorithms [17], Inverse SAR (ISAR) processing [18], or recently published and more sophisticated techniques based on 3D Inverse Interferometric SAR (3D InISAR) [19] and its polarimetric extension [20]. To mitigate the impact of the previously mentioned issues, specifically the ones that cause a lack of contrast between vessels and surrounding sea clutter, a common strategy involves applying preprocessing to the data to optimize subsequent detection. In this context, leveraging the multiple polarizations provided by modern SAR sensors has gained significant attention and has proven effective in enhancing vessel detection capabilities using SAR images [21; 22; 23; 24]. A sub-optimal solution is to process the VH polarization (if available), as it is expected to provide greater contrast between vessels and sea areas [25]. Another approach, when quad-polarimetric data are available, is to subtract the VV component from the image SPAN [26], given that the sea response is generally stronger in this channel. Alternatively, some methods proposed different polarimetric optimizations with the goal of suppressing sea clutter while preserving the polarimetric signatures of vessels, thereby improving subsequent detection [27; 28; 29; 30]. More details about different polarimetric methods employed to improve vessel detection capabilities are given in the Discussion section (Section 5) of this manuscript. In this paper, we propose the use of a recently published and innovative polarimetric optimization [31] as a preprocessing step to enhance image contrast, thereby improving the application of target detection techniques. The core idea of this method consists in exploiting target orthogonality leveraging all available polarimetric information. By manually selecting a reference pixel (or patch) containing an undesired feature (e.g., located at the sea), image data are projected onto an orthogonal space that cancels the selected feature and enhances others (i.e., ships). Consequently, the polarimetric optimization can be generally applied to the detection of desired targets that are located on difficult environments. Here, we focus on the detection of vessels imaged by SAR sensors at different frequency bands, and we analyse the eventual improvement that the optimization provides for different combinations of polarimetric channels. Special focus is placed on the performance of the different combinations of channels found in dual-pol systems.