The rapid change and expansion of human settlements raise the need for precise remote-sensing monitoring tools. While some Land Cover (LC) maps are publicly available, the knowledge of the up-to-date urban extent for a specific instance in time is often missing. The lack of a relevant urban mask, especially in developing countries, increases the burden on Earth Observation (EO) data users or requires them to rely on time-consuming manual classification. This paper explores fast and effective exploitation of Sentinel-1 (S1) and Sentinel-2 (S2) data for the generation of urban LC, which can be frequently updated. The method is based on an Object-Based Image Analysis (OBIA), where one Multi-Spectral (MS) image is used to define clusters of similar pixels through super-pixel segmentation. A short stack (<2 months) of Synthetic Aperture Radar (SAR) data is then employed to classify the clusters, exploiting the unique characteristics of the radio backscatter from human-made targets. The repeated illumination and acquisition geometry allows defining robust features based on amplitude, coherence, and polarimetry. Data from ascending and descending orbits are combined to overcome distortions and decrease sensitivity to the orientation of structures. Finally, an unsupervised Machine Learning (ML) model is used to separate the signature of urban targets in a mixed environment. The method was validated in two sites in Portugal, with diverse types of LC and complex topography. Comparative analysis was performed with two state-of-the-art high-resolution solutions, which require long sensing periods, indicating significant agreement between the methods (averaged accuracy of around 90%).

The paper proposes a flexible and efficient wavenumber domain processing scheme suited for close formations of low earth orbiting (LEO) synthetic aperture radar (SAR) sensors hosted on micro-satellites or CubeSats. Such systems aim to generate a high-resolution image by combining data acquired by each sensor with a low pulse repetition frequency (PRF). This is usually performed by first merging the different channels in the wavenumber domain, followed by bulk focusing. In this paper, we reverse this paradigm by first upsampling and focusing each acquisition and then combining the focused images to form a high-resolution, unambiguous image. Such a procedure is suited to estimate and mitigate artifacts generated by incorrect positioning of the sensors. An efficient wave–number method is proposed to focus data by adequately coping with the orbit curvature. Two implementations are provided with different quality/efficiency. The image quality in phase preservation, resolution, sidelobes, and ambiguities suppression is evaluated by simulating both point and distributed scatterers. Finally, a demonstration of the capability to compensate for ambiguities due to a small across-track baseline between sensors is provided by simulating a realistic X-band multi-sensor acquisition starting from a stack of COSMO-SkyMed images.

In this paper we present a first demonstration of spaceborne L-band SAR Tomography over a forested scenario. To this aim, we process a SAOCOM stack of nine acquisitions with across-track baselines theoretically achieving a vertical resolution around 22 m. To obtain a correct TomoSAR imaging, we phase calibrate the stack by properly accounting for forest volumetric structure, which drives the choice of calibrating with smaller baselines. Finally, we ground steer calibrated TomoSAR to achieve a consistent reconstruction of forest structure above ground and derive a forest height map. Comparison with reference canopy height gives a very good overall agreement, proving the effectiveness of our approach and making the experiment an interesting test-bed in view of future long-wavelength SAR missions.

Along-track formations of SAR satellites are widely discussed in the literature for their ability to solve the trade-off between resolution and coverage. It is usually assumed that all the satellites in the constellation follow the same orbit with a negligible orbital tube, since cross-track (XT) baselines introduce major complexity to high-resolution wide-swath (HRWS) imaging. However, addressing the XT issue is crucial for the feasibility of future formation missions, as realistic orbit control conditions and collision risk considerations will surely impose such baselines. This work discusses HRWS imaging with non-zero XT baselines and derives an algorithm for combining the different channels. The approach differs from traditional methods because the combination is done after focusing, where each target is compressed to several pixels. In this manner, one can address the varying elevation locally. Further performance improvement is obtained by a data-driven definition of the forward model, which utilizes the Matching-Pursuit algorithm to identify the significant ambiguities in each area. The proposed method is highly flexible, as it locally adapts the solution to the actual backscatter and elevation of the scene. Validation and testing were achieved by simulations of X-band acquisition, showing promising results.

Formations of small satellites are gaining momentum as an alternative to single Synthetic Aperture Radar (SAR) systems, allowing them to improve performance and reduce costs. Each sensor operates with a low Pulse Repetition Frequency (PRF), so swath size can be kept large, and the strong azimuth ambiguities are later resolved by combining acquisitions from multiple satellites. For proper imaging to be successful, careful calibration of the system and geometric parameters is performed based on the single-channel images. However, ambiguities jeopardize the retrieval of parameters from data; therefore, it is crucial to identify those areas that are inherently ambiguity-free. This paper handles acquisitions that are highly affected by azimuth ambiguities, as in the case of the formation. First, we model the replica’s structure in the focused image and then propose an effective yet simple method to detect such disturbances. Lastly, we demonstrate an improved approach for estimating the along-track baseline by incorporating the aforementioned ambiguity detection scheme. The proposed analysis is validated with simulated data, considering a realistic reflectivity map derived from a COSMO-SkyMed stack.