In recent years, the application of deep learning (DL) has revolutionized remote sensing (RS) image analysis, allowing for the extraction of high-level features, and addressing complex tasks. However, the success of DL models relies heavily on the availability of labeled data, and acquiring labeled samples in the RS domain can be particularly challenging due to factors such as cost, time, and the dynamic nature of landscapes. Active learning (AL) has been a well-established concept in RS imagery analysis, even predating the widespread adoption of DL. Its significance lies in its ability to iteratively select the most informative samples from the unlabeled dataset, reducing the annotation cost and improving model performance with limited labeled data. In the era of DL, where the demand for labeled samples is higher than ever, AL has become increasingly crucial. Deep AL is an innovative and intricate approach that seeks to harness the strengths of both DL and AL methodologies. This integration aims to improve the performance of DL models while reducing the reliance on large amounts of labeled data. Integrating AL with deep architectures presents challenges but offers a promising approach to RS tasks.

This article presents a new scheme called 2-D mixed compressive sensing back-projection (CS-BP-2D), for synthetic aperture radar (SAR) imaging on a geocoded grid, in a single measurement vector frame. The back-projection linear operator is derived in matrix form and a patched-based approach is proposed for reducing the dimensions of the dictionary. Spatial compressibility of the radar image is exploited by constructing the sparsity basis using the back-projection focusing framework and fast solving the reconstruction problem through the orthogonal matching pursuit algorithm. An artifact reduction filter inspired by the synthetic point spread function is used in postprocessing. The results are validated for simulated and real-world SAR data. Sentinel-1 C-band raw data in both monostatic and space-borne transmitter/stationary receiver bistatic configurations are tested. We show that CS-BP-2D can focus both monostatic and bistatic SAR images, using fewer measurements than the classical approach, while preserving the amplitude, the phase, and the position of the targets. Furthermore, the SAR image quality is enhanced and also the storage burden is reduced by storing only the recovered complex-valued points and their corresponding locations.

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.

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.

Recent advances in Synthetic Aperture Radar (SAR) sensors and innovative advanced imagery techniques have enabled SAR systems to acquire very high-resolution images with wide swaths, large bandwidth and in multiple polarization channels. The improvements of the SAR system capabilities also imply a significant increase in SAR data acquisition rates, such that efficient and effective compression methods become necessary. The compression of SAR raw data plays a crucial role in addressing the challenges posed by downlink and memory limitations onboard the SAR satellites and directly affects the quality of the generated SAR image. Neural data compression techniques using deep models have attracted many interests for natural image compression tasks and demonstrated promising results. In this study, neural data compression is extended into the complex domain to develop a Complex-Valued (CV) autoencoder-based data compression for SAR raw data. To this end, the basic fundamentals of data compression and Rate-Distortion (RD) theory are reviewed, well known data compression methods, Block Adaptive Quantization (BAQ) and JPEG2000 methods, are implemented and tested for SAR raw data compression, and a neural data compression based on CV autoencoders is developed for SAR raw data. Furthermore, since the available Sentinel-1 SAR raw products are already compressed with Flexible Dynamic BAQ (FDBAQ), an adaptation procedure applied to the decoded SAR raw data to generate SAR raw data with quasi-uniform quantization that resemble the statistics of the uncompressed SAR raw data onboard the satellites.

In this paper, we introduce a compressive sensing-based approach for increasing bistatic synthetic aperture radar imaging quality in the context of multi-aperture acquisition. The analyzed data is recorded over an opportunistic bistatic setup including a stationary ground based receiver (COBIS) and Sentinel 1 C-band transmitter. Since the terrain observation by progressive scans mode is operated, the receiver is able to record synchronization pulses and echoed signals from the scene during many apertures. Hence, it is possible to improve the azimuth resolution by exploiting the multi-aperture data. Obviously, the recorded data is 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. Therefore, a matching pursuit compressive sensing algorithm is employed for filling the “gaps” occurring in Doppler spectrum. The sparsifying basis/dictionary is constructed using the synthetic generated azimuth chirp. The obtained results show a significant improvement of the azimuth resolution along with dramatic attenuation of the side-lobes.