Synthetic Aperture Radar (SAR) represents nowadays a key technology in Earth Observation (EO), evolving its original capabilities into both large-scale monitoring of geophysical parameters and very high-resolution imaging with short revisit times. The increase in system performance and the wide range of application scenarios require significant efforts in the design of current and future SAR missions: one of the most critical on-board operations is the digitization of the received echoes, directly impacting the final image quality and, at the same time, limited by the available downlink capacity. State-of-the-art quantization methods, such as Block Adaptive Quantization (BAQ), offer a good trade-off between signal quality and overall complexity but lack adaptivity to the imaged scenario. This leads to different impacts of the quantization error on the final SAR image. As an evolution of BAQ, Performance-Optimized BAQ (PO-BAQ) is a recently proposed quantization method, which addresses this issue by employing variable quantization rates across the scene, targeting specific performance requirements in the final SAR image. In this paper, we present a feasibility study of variable bitrate allocation in a realistic SAR mission scenario: to ensure flexibility, we consider the bitrate allocation map (BRM) to be uploaded at commanding phase during each ground segment contact, individually tailoring the required performance quality for each acquisition. State-of-the-art uplink data rates are considered, and the complete performance evaluation after SAR processing is carried out using the experimental on-board processor developed within the SOPHOS Horizon 2020 project.

Multichannel synthetic aperture radar (MC-SAR) allows for high-resolution imaging of a wide swath (HRWS), at the cost of acquiring and downlinking a significantly larger amount of data, compared with conventional SAR systems. In this letter, we discuss the potential of efficient data volume reduction (DVR) for MC-SAR. Specifically, we focus on methods based on transform coding (TC) and linear predictive coding (LPC), which exploit the redundancy introduced in the raw data by the finer azimuth sampling peculiar to the MC system. The proposed approaches, in combination with a variable-bit quantization, allow for the optimization of the resulting performance and data rate. We consider three exemplary yet realistic MC-SAR systems, and we conduct simulations and analyses on synthetic SAR data considering different radar backscatter distributions, which demonstrate the effectiveness of the proposed methods.

Next-generation SAR systems will be capable of performing high-resolution, wide-swath acquisitions at frequent revisit times. The overcoming of conventional SAR limitations will also lead to the generation of very large volumes of onboard data which need to be stored and managed by the system and downlinked to the ground. This poses severe constraints in terms of onboard memory requirements and downlink capacity and, in this challenging scenario, the onboard quantization of SAR raw data represents a crucial aspect, acting as a trade-off between the achievable product quality and the resulting onboard volume of data. State-of-the-art quantization schemes allow for enhanced data rate allocation, however, the optimization is directly performed on raw data, without targeting a desired performance on the final higher-level SAR/InSAR product. In this paper, we investigate the use of artificial intelligence (AI), and in particular of deep learning (DL), for developing a flexible onboard SAR raw data quantization method, with the aim of deriving an optimized and fully adaptive data rate allocation given a set of desired performance metrics and requirements in the resulting focused SAR and InSAR products, without relying on a priori information on the acquired scene. Different performance parameters are considered, such as the signal-to-quantization noise ratio (SQNR), the phase errors, the InSAR coherence loss as well as the resulting noise equivalent sigma zero (NESZ), extending the capabilities of the architecture to provide multiple bitrate estimations for a single input scene at the same time, depending on the desired application case. We use experimental TanDEM-X bistatic SAR data, both for the training of the DL model as well as for the validation and demonstration of the suitability of the proposed method. In view of a potential onboard implementation, a possible hardware architecture for the proposed compression scheme is investigated as well.

Present and next-generation synthetic aperture radar (SAR) missions require an increasing volume of onboard data, due to the employment of large bandwidths, multiple channels and polarizations, and large swath widths acquired by bi- and multi-static sensor configurations. This leads to stringent requirements in terms of onboard memory and downlink capacity, hence making the proper quantization of the SAR raw data represents an task of utmost importance, as it affects the amount of data but also the quality of the SAR and InSAR products. This paper presents novel methods for efficient SAR raw data compression, which make use of artificial intelligence for the joint optimization of bitrate allocation and the resulting performance and exploit the potential of transform and predictive coding schemes for data volume reduction in the context of multi-azimuth channel (MAC) SAR. Simulations and analyses on real data are presented, showing the suitability of the proposed methods.

Next generation SAR systems will bring a huge improvement in terms of SAR performance and coverage through the use of large bandwidths and digital beamforming techniques in combination with multiple acquisition channels. This will allow for overcoming the limitations imposed by conventional SAR imaging for the acquisition of wide swaths and, at the same time, of finer resolutions. The significant improvements that can be achieved in terms of performance are associated with the generation of large volumes of data, which, in turn, set harder requirements for the onboard memory and downlink capacity of the system.In this scenario, an efficient quantization of SAR raw data is of crucial importance, as it defines the amount of onboard memory and it directly affects the quality of the generated SAR products. In the Performance-Optimized BAQ (PO-BAQ), the basic concept of the original BAQ is further extended according to the approach proposed in [1], which represents a first attempt for an optimization of the resource allocation depending on the performance requirement defined for the final higher-level SAR/InSAR product. As quantization errors are significantly influenced by the local distribution of the SAR intensity, such an optimization is achieved by exploiting the a priori knowledge of the SAR backscatter statistics of the acquired scene.In this contribution we investigate the feasibility of deriving a Performance-Optimized bitrate map through a machine learning-based architecture, in view of a future possible onboard realization.

Present and future spaceborne synthetic aperture radar (SAR) missions are designed to acquire an increasingly large amount of onboard data. This is a consequence of the use of large bandwidths, multiple polarizations, and the acquisition of large swath widths at fine spatial resolutions, which result in challenging requirements in terms of onboard memory and downlink capacity. In this scenario, SAR raw data quantization represents an essential aspect, as it affects the volume of data to be stored and transmitted to the ground as well as the quality of the resulting SAR products. Dynamic predictive block-adaptive quantization (DP-BAQ) is a novel technique, recently proposed by the authors, consisting of a low-complexity data compression method, and its application is particularly suitable for staggered SAR systems. DP-BAQ exploits the existing correlation among the azimuth raw data samples by applying linear predictive coding (LPC). This results in a data rate reduction of up to 25% with respect to state-of-the-art SAR quantization methods. In this letter, we test and validate the potential of DP-BAQ on airborne SAR data which emulates the system scenario of Tandem-L, a German Aerospace Center (DLR) mission proposal for a bistatic L-band system. For this purpose, an experimental SAR image has been acquired at the L-band by the airborne DLR flugzeug-SAR (F-SAR) sensor over the Kaufbeuren area, in Southern Germany. In order to simulate the staggered SAR acquisition mode, we implemented a dedicated resampling and filtering of the data. Our analyses confirm the effectiveness of DP-BAQ for efficient data volume reduction, exhibiting a consistent and promising performance when tested on areas characterized by different land cover types and backscatter statistics.

Frequency scanning (FScan) is an innovative acquisition mode for synthetic aperture radar (SAR) systems. The method is based on the frequency-dependent beam pointing capabilities of phased array antennas, artificially increased via the combined use of true time delays and phase shifters within the array antenna. By this, typical limitations of conventional SAR systems in terms of achievable swath width and azimuth resolution can be mitigated, and so a wide swath can be imaged maintaining a fine azimuthal resolution. In the first part of the article, we introduce the theoretical concept, which is necessary to evaluate the reduced echo window length (EWL) with respect to equivalent stripmap data and the implications for the transmit pulse characterization. An FScan sensor flying in a TerraSAR-X-like orbit is shown to be capable of imaging an 80-km wide swath with 1-m azimuth resolution. The resulting time–frequency properties of the recorded raw data make the traditional SAR data compression algorithms such as block-adaptive quantization (BAQ) highly inefficient in this case. Therefore, the second part of the article investigates dedicated quantization methods for efficient data volume reduction in FScan systems. Different solutions are investigated and evaluated through simulations. Various transformations of the raw data have been exploited to optimize the encoding process, including deramping, fast Fourier transform (FFT), and blockwise approaches. Compared with standard BAQ in the time domain, the suggested data compression methods significantly improve the resulting signal-to-quantization noise ratio, allowing for the reduction in the overall data volume by about 60% for the considered system scenario, while maintaining robustness in the presence of inhomogeneous scene characteristics at the cost of a modest complexity increase for its on-board implementation.

For the design of present and next-generation spaceborne SAR missions, constantly increasing data rates are being demanded, which impose stringent requirements in terms of onboard memory and downlink capacity. In this scenario, the efficient quantization of SAR raw data is of primary importance, since the utilized compression rate is directly related to the volume of data to be stored and transmitted to the ground and, at the same time, it affects the resulting SAR imaging performance. In this paper, we introduce the performance-optimized block-adaptive quantization (PO-BAQ), a novel approach for SAR raw data compression which aims at optimizing the resource allocation and, at the same time, the quality of the resulting SAR and InSAR products. This goal is achieved by exploiting the a priori knowledge of the local SAR backscatter statistics, which allows for the generation of high-resolution bitrate maps that can be employed to fulfill a predefined performance requirement. Analyses on experimental TanDEM-X interferometric data are presented, which demonstrate the potentials of the proposed method as a helpful tool for performance budget definition and data rate optimization of present and future SAR missions.

Staggered synthetic aperture radar (SAR) is an innovative SAR acquisition concept which exploits digital beamforming (DBF) in elevation to form multiple receive beams and continuous variation of the pulse repetition interval to achieve high-resolution imaging of a wide continuous swath. Staggered SAR requires an azimuth oversampling higher than an SAR with constant pulse repetition interval (PRI), which results in an increased volume of data. In this article, we investigate the use of linear predictive coding, which exploits the correlation properties exhibited by the nonuniform azimuth raw data stream. According to this, the prediction of each sample is calculated onboard as a linear combination of a set of previous samples. The resulting prediction error is then quantized and downlinked (instead of the original value), which allows for a reduction of the signal entropy and, in turn, of the onboard data rate achievable for a given target performance. In addition, the a priori knowledge of the gap positions can be exploited to dynamically adapt the bit rate allocation and the prediction order to further improve the performance. Simulations of the proposed dynamic predictive block-adaptive quantization (DP-BAQ) are carried out considering a Tandem-L-like staggered SAR system for different orders of prediction and target scenarios, demonstrating that a significant data reduction can be achieved with a modest increase of the system complexity.

Nonlocal algorithms have been proven to be a very effective tool for the reconstruction of the interferometric phase field in synthetic aperture radar images. The offset-compensated nonlocal filtering is a recent concept proposed to cope with the problem of the rare patch effect which arises especially in presence of terrain slopes with a large topography variation. In this paper we assess the quality of different Digital Elevation Models (DEMs) generated by varying the phase estimation algorithm. In the specific, we aim at analyzing the performance of the Offset-Compensated InSAR-BM3D filter with respect to state-of-the-art nonlocal filters, i.e. the InSAR-BM3D and the NLSAR filters. We perform experiments on real TanDEM-X data and exploit a high-resolution LiDAR DEM over the Austrian Alps in order to analyze the filter performance in terms of DEM's residual height error and details preservation.