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.

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.

With the increase of high spatial and temporal resolution SAR data availability, novel applications and information extraction techniques become possible. Among these, the extraction of micro-motion information has the potential to unlock a range of applications, such as infrastructure monitoring, maritime surveillance and natural disaster damage assessment. However, sensors, acquisition modes and products have not been designed with in mind the optimization of micro-motion extraction and its applications, therefore, careful considerations need to take place when selecting the most suitable data and designing processing algorithms. In this paper practical and processing considerations when dealing micro-motion extraction from high-resolution SAR sensors are discussed and supported with experimental results obtained from Capella, Umbra and TerraSAR-X data.

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.

The present paper provides a focus on the ROSE-L instrument internal calibration (iCAL) design. The ROSE-L SAR Instrument has been conceived to realize a full Digital Beam Forming (DBF) architecture, requiring an innovative iCAL approach.

The present paper provides an overview of the ROSE-L instrument architecture and functionality, with emphasis on internal calibration, mode design, and overall SAR performance. In particular, all the SAR modes have been described and relevant (simulated) performance has been presented.Moreover, a sequential internal calibration concept is also proposed and discussed.

Synthetic Aperture Radar (SAR) is becoming more and more requested in the commercial and scientific world, especially considering the latest developments toward compact, high- resolution and cost-effective sensors. Since 2008, MetaSensing has been commercially offering services and sensors allowing a larger group of users to benefit from the advantages of SAR technology. During the last year of operation, MetaSensing has remarkably extended its radar capabilities by performing several airborne campaigns at different frequencies and interfer-ometric and polarimetric configurations. This paper reports about technical specifications of a selection of airborne data acquisitions at L, X and Ku band.