AI-BAQ: Deep Learning for Adaptive SAR Raw Data Quantization

Next-generation (NG) synthetic aperture radar (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 tradeoff between the achievable product quality and the resulting onboard volume of data. State-of-the-art (SoA) 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/interferometric SAR (InSAR) product. In this article, 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.