# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 3 Study Cases
### Application 6: Earth Observation for Vessel Detection  
Marine vessels detection is currently a very hot topic because it represents a key asset in different domains, e.g., maritime traffic monitoring, surveillance, and smuggling (e.g., [253]). Those vessels that do not voluntarily report any information about their identity, vessel type, route, etc. can be detected and tracked only under a non-cooperative system. Radars or visual surveillance standard methods are usually adopted for vessel non-cooperative detection. A non-cooperative system, which calls for all-day and all-weather capabilities, large area coverage, dense revisiting times, and fine spatial resolution details, is thus a key asset to provide added-value products for the end-users (e.g., policymakers, local authorities, etc.) [254]. Vessels SAR observation is a well-established and mature application, and it is unanimously recognized as one of the most suitable remote sensing tools because it can meet most of the above-mentioned requirements, in addition to operating in all weather and lighting conditions (e.g., [255; 256; 257; 258]). Indeed, most of these systems work in the X-band of SAR payload, with resolutions of the order of the meters, such as for instance the TerraSAR-X satellite, and its twin TANDEM-X [259; 260]. Very high resolution (VHR) instruments are able to provide extremely detailed images of the observed scenes (e.g., [261]). The limitations of SAR satellites, such as the long revisiting time, are overcome by using a constellation of micro and nano satellites, which are able to provide VHR images with revisiting times of the order of 2 to 3 h, and at lower costs (e.g., [84; 262]), such as Capella X-SAR and ICEYE constellations [57]. In SAR images, the vessel signature appears as a spot brighter than the background due to the vessel scattering, i.e., direct reflection from areas perpendicular to the radar beam, corner reflections, and multiple reflections from the vessel and sea surface [255]. In addition, when imaged by fine spatial-resolution SARs and under specific weather-marine conditions, waves associated with moving vessels can be also observed in the data. However, radar images suffer from large intrinsic noise (speckle), in addition to the azimuth displacements (\"train-off-the-track effect\"), which are caused by the Doppler effect, and the azimuth ambiguity patterns (\"ghost\"), which are weaker, repetitive artefacts appearing in coastal areas, caused by the presence of brighter objects on the mainland (e.g., [263]). False positive detections can occur in non-homogeneous areas of the images due to changes in ocean backscattering or to oceanographic phenomena, e.g., atmospheric fronts, internal waves, current boundaries, breaking waves, outlying rocks, shoal sea currents, and coastal effects, while high wind and rough sea states can hamper the correct identification of small vessels [255; 264]. The comparison of the pre- and post-event images collected by the micro-satellites can provide an efficient method to detect map changes, robust both to speckle phenomena and co-registration problems (e.g., [265]). In order to increase the accuracy of vessel detection, X-band SAR data are processed through four fundamental stages: land masking, pre-processing, pre-screening, and discrimination [255; 266]. Such processing allows the presence of a non-negligible coherent component in the backscattered sea surface signal to be highlighted by evaluating the corresponding Rice Factor (RF) image of the area under study [267]. The RF represents the coherent-to-incoherent received power ratio of the backscattered signal, and therefore the pre-processing stage consists in evaluating the RF of the calibrated SAR image. The pre-screening stage is based on a vessel search by applying the \"bright anomalies\" method on the RF images [255; 268]. Because it was proven that the RF image follows a log-normal distribution, a threshold can be straightforwardly set according to a constant false alarm rate (CFAR) approach to remove false positives [266].