Distributed Synthetic Aperture Radar (DSAR) systems represent an appealing solution to lower the overall costs and improve flexibility and efficiency of future Earth Observation missions. However, this type of systems requires receivers to fly very close to each other for enabling coherent beamforming techniques. Autonomous formation flying strategies can be employed to keep satellites within a small envelope. However, the variation over time of the distances among the satellites, that stems from the relative dynamics associated to the need of ensuring a safe formation geometry, results in a performance degradation for the beamforming over specific areas or time spans. The paper analyzes the problem of DSAR performance prediction when the satellite formation is designed exploiting the safety ellipse concept.

Along with the monostatic radar imaging configuration, as a standard approach for Synthetic Aperture Radar imaging, new generation ideas of radar imaging geometry under the name of bistatic configurations are emerging. Due to the separation of the transmitter and receiver positions in bistatic SAR systems, different configurations can be imagined for them. One of these configurations, which is more well-known, is the azimuth-invariant configuration. In this configuration, the receiver follows the transmitter in a fixed azimuth path, collates the ground targets’ response in the forward-looking mode and at different angles, and generates the final SAR image. This SAR image is comparable to the monostatic in various aspects such as resolution, signal-to-noise ratio (SNR), etc. In this paper, some of these aspects will be evaluated. The results show with the increase of bistatic angle, both of the resolution and SNR are worse.

Artificial Intelligence (AI) applications on Earth Observation (EO) satellite data, such as those for vessel detection, are gaining attention for their potential to meet strict bandwidth and latency requirements. While traditional on-ground computing pipelines often rely on heavy post-processing, implementing these techniques onboard satellites is challenging due to limited computing resources. To support the development of efficient onboard data processing strategies, this study compares the performance of object detection on raw data from Sentinel-2 and VENμS missions. The study demonstrates that the proposed two-stage approach with a focus on efficiency is capable of identifying vessels in raw data with minimal pre-processing. Specifically, our method achieved a remarkable Average Precision (AP) of 0.841 on the VENμS dataset.

This paper focuses on the timing analysis for a Distributed Synthetic Aperture Radar (DSAR) system exploiting an opportunity illuminator. This is the case of RODiO, which is a new mission concept funded by the Italian Space Agency (ASI) for a Phase A study in the framework of ALCOR program. RODiO’s aim is to match the growing trend towards the miniaturization of satellites and new Synthetic Aperture Radar (SAR) applications. For this reason, RODiO consists in a cluster of four receiving-only CubeSats flying in a close formation and exploiting the independent PLATiNO-1 satellite as a transmitter. Because of the nature of RODiO mission, simultaneous observations with respect to the monostatic illuminator are needed. Through the comparison of monostatic and bistatic chronograms, a timing analysis is performed in the paper in order to assess the effects of DSAR geometry on simultaneous observation opportunities, aiding the design of the system.

This paper presents the findings of the COAST project, an innovative investigation into maritime surveillance using Synthetic Aperture Radar (SAR) technology, funded by the Italian Space Agency. The research specifically delves into the synergistic potential of a multi-frequency/multi-mission (MFMM) approach to detect and identify vessels, a facet relatively unexplored in previous studies. Four technical objectives drive the study, including the detection of both visible and non-visible non-cooperating ships and their classification using SAR tomography techniques, as well as AIS/SAR data matching with velocity estimation. This research promises to enhance maritime domain awareness and security by furthering our understanding of SAR-based vessel detection and classification methodologies.

Nowadays, the use of Artificial Intelligence on board Earth Observation satellites is under investigation for applications having strict bandwidth and latency requirements, such as vessel detection. However, many of the on-ground current computing pipelines rely on data post-processing techniques whose applications onboard satellites are tricky because of their limited computing power. To enable the analysis and the research of lightweight onboard data processing techniques, we provide VDS2Raw, the first Sentinel-2 Raw dataset for vessel detection applications. Finally, we also compared different object detection Deep Learning techniques in terms of detection performance and inference time to perform a feasibility analysis of performing onboard vessel detection on raw multi-spectral data.

The wakes generated by moving vessels represent relevant patterns in remotely sensed images. They are a marker of ship presence and can be processed to infer route, speed, size, and type of ships. Automatic wake detection can be exploited by law enforcement agencies and local authorities for ensuring a wide range of applications, including maritime traffic surveillance, border control, and protection of marine protected areas. The topic is thus attracting increasing interest from the remote sensing community. This paper contributes in this context presenting a novel approach based on the detection of the keypoints of wake components by Convolutional Neural Networks (CNNs) in electro-optical satellite imagery. The selected approach to deep learning relies on a transfer learning procedure fine-tuning the ImageNet weights. This is performed through an ad-hoc developed dataset realized from Sentinel-2 multi-spectral images and Automatic Identification System (AIS) data in northern Europe. The experimental results confirm the robustness of the proposed method, which is tested against different spectral bands from visible to near-infrared and also by a domain shifting on lower resolution Landsat-9 images. Fractional errors in the positioning of the wake vertex are lower than 10% and the achieved heading accuracy is below 10°. The proposed method is faster than the traditional approaches based on Radon Transform, and due to its lightweight nature, our model can be executed efficiently on edge-AI devices, enabling real-time processing onboard.

Maritime trade and trasport occupy a pivotal position in the current era of globalization. Thus, monitoring ships at sea represents the starting point of this paper in which a novel approach to detect ships by wake has been proposed, based on Instance Segmentation deep learning architecture Mask R-CNN. In order to train and test this network, 766 wake chips cropped from 50 multispectral images acquired from Sentinel-2 satellites were observed. In particular, B2 (blue), B3 (green), B4 (red) and B8 (Infrared) bands were considered since they are all characterized by same resolution. The results proved that Mask R-CNN is capable to detect the vast majority of ship wakes with high confidence percentage in different configurations, i.e. slanted wakes, multiple wake scenarios or wakes in dark areas not related to their features.

The recent launches of Earth Observation (EO) satellites have made numerous SAR images available to dynamically monitor the ocean with improved spatial resolution at shorter revisit time. Focusing on automatic target detection of ships with the specific aim of improving our Maritime Domain Awareness (MDA), this work assesses the capabilities of multi-frequency/multi-mission spaceborne Synthetic Aperture Radar (SAR) data. Specifically, Sentinel-1 (C-band), COSMO-SkyMed (X-band), and SAOCOM (L-band) missions have been considered in this analysis. The aim of the paper is to present an efficient approach for interpreting images acquired within small time gaps, ensuring fisheries and pollution control, anti-piracy actions, and surveillance over coastal/protected regions.

The paper shows the strong potentialities of multimission/multifrequency SAR data for improving the maritime situational awareness in coastal areas. Two main issues are analyzed: the detection of ships that are visible in SAR images and the identification of non-collaborative vessels, which are not visible in SAR images. In the first case, the multimission/multifrequency data guarantees: (a) smaller revisit time with respect to a single mission, enabling cross-check of the detection in several images and, thus, improving the detection rate, and (b) the availability of images covering large areas at low resolution as well as smaller swath observed with higher resolution. This is crucial in particular for the coastal areas where local phenomena can strongly affect the detection performance. In the second case, the multimission/multifrequency data enables innovative approaches exploiting the different appearance of ship and its wake at different frequencies.

The paper focuses on the concept of a formation-flying synthetic aperture radar (FF-SAR) bistatic system composed of a set of compact, low-weight satellite receivers in close formation (within 1 km) placed in the same low-Earth orbit at large distance (about 100 km) from a transmitter. Each receiver is conceived to fit a 12-unit CubeSat. A signal model adapted to the proposed formation geometry is also presented, and a corresponding processing scheme to achieve range swath widening and signal-to-noise ratio (SNR) improvement is illustrated and discussed.

This paper discusses a new receiving antenna for remote sensing applications to be mounted onboard a formation-flying synthetic aperture radar (FF-SAR) bistatic system based on the CubeSat standard. The formation works as a bistatic SAR collecting microwave signals coming from a transmitting SAR unit. The receiving antenna has been designed according to the acquisition modes of the formation, namely a stripmap mode for signal-to-noise ratio improvement and a High-Resolution Wide-Swath mode for the monitoring of large regions. In order to meet the very different requirements for both operating modes with the physical constraints imposed by the nanosat geometry, a large reflector with reconfigurable feed has been conceived and simulated. The results show that the proposed antenna accomplishes the design specifications.

This paper presents a segmentation algorithm for marine synthetic aperture radar (SAR) images. The proposed approach is based on the analysis of image sublooks along azimuth direction and exploits a novel indicator, called incoherent entropy, to divide the input targets into three classes, namely, ship, sea, and ambiguity. A global threshold detector is then implemented to discriminate between stable targets (ships), providing low incoherent entropy amplitude, and ambiguity or sea, implying very high and high values of the indicator, respectively. An integration of the segmentation algorithm into maritime surveillance systems is investigated, proposing its use as a discrimination tool to limit the false alarm rate of traditional prescreening algorithms. The main steps of a standard SAR-based ship detection system are thus implemented to provide a first screening of the candidate targets, which are, then, processed by sublook analysis using incoherent entropy to identify false detections. The proposed technique is tested on stripmap SAR images collected by the TerraSAR-X mission, over the Gulf of Naples, Italy. Algorithm detections are validated using ground-truth data, provided by automatic identification system. The results confirm the capability of the proposed discrimination approach to reject the vast majority of the false detections resulting from the prescreening algorithm.

Spaceborne synthetic aperture radar (SAR) can be considered an operational asset for maritime monitoring applications. Well-assessed approaches exist for ship detection, validated in several maritime surveillance systems. However, measuring vessel velocity from detected single-channel SAR images of ships is in general difficult. This letter contributes to this problem by investigating the possibility of retrieving vessel velocity by wake analysis. An original method for velocity estimation is developed for calm sea (Beaufort scale 1-2) and applied over seven X-band SAR images, gathered by COSMO-SkyMed mission over the Gulf of Naples, Italy. The algorithm exploits the well-known relation between the wavelength of the waves composing the Kelvin pattern and the ship velocity. But the proposed approach extends the applicability of the existing wake-based techniques since it foresees evaluation of the wavelength along a generic direction in the Kelvin angle. Promising results have been achieved, which are in good agreement with those of more assessed techniques for ship velocity estimation in SAR images.

A targets detection algorithm is proposed for maritime surveillance by single-channel SAR images. It foresees a preliminary prescreening step, carried out using an adaptive threshold algorithm, followed by a discrimination phase, performed by sub-look analysis. The latter discriminates the pixels detected by the former step in three classes, i.e. targets, sea, and azimuth ambiguity. The algorithm is tested on single channel StripMap TerraSAR-X data. Results indicate that the selected algorithm shows promising detection performance and ambiguity rejection capabilities.

Since the ship wakes appear in SAR images as dark/bright lines, they are typically identified by using line detectors, such as the Radon Transform. The most assessed techniques imply the thresholding approach in the Radon domain in order to recognize peaks and troughs representative of ship features. In this ambit, the paper shows an application of an original approach for wake detection in high-resolution SAR images, conceived to be adequate for inclusion in an automatic maritime surveillance procedure. In the proposed algorithm, the peaks/troughs are detected and recognized as representative of the ship wakes only if their appearance is compatible with the physical characteristics of the wakes provided by the hydrodynamic theory. In order to estimate its performance, it is applied on 11different wake appearances imaged in the Gulf of Naples by 3 passages of COSMO/SkyMed and one passage of TerraSAR-X in stripmap working mode under different polarizations. The results show that the proposed algorithms well recognizes the wake features, also in scenarios with very close wakes or with land and azimuth ambiguities bright returns.

The study of a novel navigation system for Unmanned Airborne System (UAS) based on bistatic Synthetic Aperture Radar (SAR) is presented. The innovative bistatic configuration builds on spaceborne radar transmitters and airborne receivers, the latter mounted in a forward-looking geometry. Such approach, impossible or extremely demanding with a monostatic approach, allows one to achieve dual information with two different radar working modes: imaging capability can be in fact coupled with the possibility of moving target indication. The study is particularly suited on one of the most common UAS platforms: a close range, medium takeoff weight, with an endurance of roughly 7 hours and cruise speed of about 50m/s, whose requirements have been identified. The finalization of the study is achieved by the definition of a strawman system concept with different approaches. Four options are identified, with different performance and system complications/challenges. The study herein reported was carried out under ESA contract 22449/09/F/MOS.