Advanced Synthetic Aperture Radar (SAR) systems acquire a large volume of data, which necessitates the development of efficient data compression methods, beyond the current conventional techniques. Sentinel-1, as one the most popular SAR missions, provides global freely accessible data. However, the available raw data (i.e., Level-0 products) are quantized before being transferred, thus the statistics are different, hindering the validation of new algorithms mainly based on machine/deep learning paradigms. To enable elaboration of further SAR raw data compression, in this study, we propose a procedure to add random quantization noise to the decoded Sentinel-1 SAR raw data in order to obtain adapted uniformly quantized raw data that resemble the statistics of the uncompressed SAR raw data onboard the satellites. This method opens further opportunities to create large benchmarks for SAR raw data for data compression and other applications. The performance of data compression techniques (Block Adaptive Quantization (BAQ) and a complex-valued autoencoder-based data compression scheme) is evaluated on the adapted uniformly quantized raw data, and the effectiveness of the defined procedure is demonstrated.

In this work, the perspective of micro-Doppler analysis from synthetic aperture radar (SAR) images is assessed for large-scale areas. We propose a processing chain that implies a coarse vibrometry estimation based on azimuth sub-aperture decomposition and local Doppler centroid computations, followed by an in-depth analysis based on conventional SAR micro-Doppler algorithms. The methdology is assessed quantitatively using simulated data in keeping with Sentinel-1 IW imaging parameters.

In this work, the perspective synergy of Ground-Based Real Aperture Radar (GBRAR) measurements and micro-Doppler analysis of space-borne Synthetic Aperture Radar (SAR) images for the monitoring of vibration frequencies of large viaduct is discussed. A methodology for the processing GBRAR data and merging of ground-based and space-borne data is described. GBRAR data are interferometrically processed to provide time-range maps of Line-of-Sight (LoS) displacements. First results of GBRAR Ku-band measurements are presented. Vibration frequencies of different structural elements of the viaduct are derived. As a co-product, displacements of the bridge deck due to the crossing of vehicles are also obtained.

In this paper, an efficient routine for elliptical grid generation employed for the back-projection sped-up is proposed. The grid accommodates the particular bistatic setup formed by a space-born transmitter (Sentinel-l) and a ground-based stationary receiver (COBIS). Herein, the adapted elliptical grid is designed such that the computational complexity of the standard back-projection algorithm used for SAR image formation decreases. Specifically, the computational load is mitigated by reducing the density of the points in the cross-range direction. Such an elliptical grid leads to the formation of the SAR image in any arbitrary plane, preserving the range–azimuth (cross-range) spectrum of the final SAR image, making it suitable for further Doppler processing algorithms (e.g., common band selection in SAR interferometry).

In this work, a synthetic aperture radar imaging system is evaluated. The acquisition scenario involves a Pulson P440 UWB-radar and a localization system based on stereo-cameras. The main goal of the sensing system is to image areas behind walls. However, since the SAR acquisition is performed by a human operator and considering the positioning errors, our focus is to compensate for these errors by developing an autofocus SAR procedure. Emergency and military are the envisaged applications. The technique proposed in this paper relies on a strong point scatterer and it is validated on both simulated and real-world data.

Recent studies revealed that the time-domain synthetic aperture radar (SAR) processors are more appropriate for future innovative SAR missions (e.g., ROSE-L, Harmony) not only due to their ability to form the SAR image on user-defined regions of interest (ROIs) but also for the straightforward accommodation to configurations wherein the azimuth spectrum folding occurs (e.g., TOPSAR). In this letter, we propose an accelerated Back-Projection (BP) SAR processor working in conjunction with a fast routine for generating the elliptical grid (laying on arbitrary planes) necessary for the sub-aperture based BP speed-up. The proposed workflow forms all the sub-aperture SAR images on the same coarse elliptical grid which before the coarse-to-fine grid interpolation are translated in the azimuth base-band. The fine-resolution SAR image is obtained by coherently integrating the sub-aperture images together with the concatenation of the corresponding fraction from the azimuth spectrum (unfolding) making the Single Look Complex (SLC) outcome proper for further Doppler-based processing. Our validation experiments indicate that the most suitable family of imaging planes is the one containing the receiver (fan-like grid) on bistatic scenarios with relatively large transmitter-receiver separation. The processing gain has been enhanced by one order of magnitude under low amplitude and phase distortions.

The Sentinel-1 SAR images acquired using the TOPSAR modes i.e., IW and EW on cross-polarization are significantly affected by the thermal noise on low-back-scattering areas. For example, in the arctic and some desert zones both inter- swath and inter-burst noise amplification occurs. In this paper we propose a workflow for removing the thermal noise from Sentinel-1 ground detected SAR images on low back-scattering conditions by employing the co-polarization SAR image and the Gaussian Process Regression. Our processing flow uses the noise vectors provided in the European Space Agency (ESA) ground detected product and scales them such that a slightly over-denoised image is produced. Then, the Gaussian Process Regression is used to map the co-polarization SAR image into the cross-polarization SAR image. Prior to this step, a radiometric correction is applied on the co-polarization data, since its pixel values are heavily dependent on the incidence angle. Finally, the denoised cross-polarization image is obtained as a linear combination between the over-denoised version and the predicted image. Since, the co-polarization channel is employed for the prediction of the missing values in the cross-polarization channel there is no need for co-registration and the de noising procedure is trustworthy.

This article introduces a compressive sensing (CS)-based approach for increasing bistatic synthetic aperture radar (SAR) imaging quality in the context of a multiaperture acquisition. The analyzed data were recorded over an opportunistic bistatic setup including a stationary ground-based-receiver opportunistic C-band bistatic SAR differential interferometry (COBIS) and Sentinel-1 C-band transmitter. Since the terrain observation by progressive scans (TOPS) mode is operated, the receiver can record synchronization pulses and echoed signals from the scene during many apertures. Hence, it is possible to improve the azimuth resolution by exploiting the multiaperture data. The recorded data are not contiguous and a naive integration of the chopped azimuth phase history would generate undesired grating lobes. The proposed processing scheme exploits the natural sparsity characterizing the illuminated scene. For azimuth profiles recovery greedy, convex, and nonconvex CS solvers are analyzed. The sparsifying basis/dictionary is constructed using the synthetically generated azimuth chirp derived considering Sentinel-1 orbital parameters and COBIS position. The chirped-based CS performance is further put in contrast with a Fourier-based CS method and an autoregressive model for signal reconstruction in terms of scene extent limitations and phase restoration efficiency. Furthermore, the analysis of different receiver-looking scenarios conducted to the insertion in the processing chain of a direct and an inverse Keystone transform for range cell migration (RCM) correction to cope with squinted geometries. We provide an extensive set of simulated and real-world results that prove the proposed workflow is efficient both in improving the azimuth resolution and in mitigating the sidelobes.

In this paper are presented target displacement measurements using synthetic aperture radar interferometry, with the Pulson P440 radar module. The synthetic aperture is formed by automatically moving the radar with the help of a robotic arm. We show that by applying an appropriate interferometric phase correction, we can measure target displacement with the Pulson P440.

In this paper is presented a new methodology for synthetic aperture radar images focusing called bidimensional mixed compressive sensing back-projection (CS-BP-2D). Spatial compressibility of the radar images is exploited by constructing the sparsity basis using the backprojection focusing framework and solving the reconstruction problem by means of the orthogonal matching pursuit algorithm (OMP).

The short revisit time and large coverage of the Sentinel-1 sensor have opened the way for countrywide deformation maps through persistent scatterer interferometry techniques. It is impossible for a human operator to manually verify millions of data points, hence the need for automatic analysis methods. In this paper, we expand on previous work by employing the newly developed uniform manifold approximation and projection dimensionality reduction methodology to deformation profiles, in a supervised manner, and we show that it is possible to identify certain types of profiles.

In this paper we analyze the possibility of detecting anomalous deformation profiles using supervised and unsupervised machine learning. For this we construct a simulated data set with deformation profiles, using canonical functions that model different deformation scenarios specific to possible hazardous phenomena in an urban environment. We present results for detecting anomalous profiles in the simulated data set and in real data using a clustering approach and a classifier trained on simulated deformation profiles.

This paper presents results obtained by applying the maximum entropy method to image reconstruction of C-band ground-based synthetic aperture radar images. In GB-SARs, azimuth resolution is dependent on the range to target. Hence, a range dependent point spread function is synthesized. Experimental results show that through the maximum entropy method target detection is enhanced resulting in both side lobes reduction and range resolution improvement.