Efficient On-Board Quantization and Data Reduction Methods for Present and Next-Generation SAR Systems: Recent Advances and Future Perspectives

Abstract

Synthetic aperture radar (SAR) represents nowadays a well-established technique for a broad variety of remote sensing applications, being able to acquire high-resolution images of the Earth's surface, independently of daylight and weather conditions. In the last decades, innovative spaceborne radar techniques have been proposed to overcome the limitations which typically constrain the capabilities of conventional SAR for the imaging of wide swaths and, at the same time, of fine spatial resolutions. In addition to that, present and future spaceborne SAR missions are characterized by the employment of multi-static satellite architectures, large bandwidths, multiple polarizations, and shorter revisit time. This inevitably leads to the acquisition of an increasing volume of on-board data, which poses hard requirements in terms of on-board memory and downlink capacity of the SAR system. This paper presents an overview of the efficient raw data quantization and data volume reduction methods which have been developed at the Microwaves and Radar Institute of DLR in the last years. In particular, we focus our attention on multi-azimuth channel (MAC) SAR and staggered SAR: for such systems, a pulse repetition frequency (PRF) typically higher than the processed Doppler bandwidth is selected for system design constraints. The resulting oversampling and correlation properties of the azimuth SAR raw signal can be exploited by applying an efficient encoding and digitization of the SAR raw data in order to reduce the on-board data volume. Simulation results show that the proposed methods allow for a significant reduction of the data volume to be downlinked to the ground at the cost of a modest increase of on-board computational effort. Further, we investigate opportunities for data volume reduction for Frequency Scanning (FScan), an innovative SAR acquisition mode which allows for high-resolution wide-swath imaging by implementing a frequency dependent (i.e. dispersive) beam pointing, which is artificially increased via the use of time delays within the array antenna. In this scenario, different solutions for on-board data volume reduction are investigated, which are based on the use of transform coding (DFT), including deramping and block-wise approaches. Compared to standard time-domain quantization approaches, the suggested data compression methods significantly improve the resulting signal-to-quantization noise ratio, allowing for the reduction of the overall data volume by about 60%. The techniques introduced so far can be combined with an efficient selection of the quantization rate used during the SAR raw data acquisition. This represents an aspect 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. We therefore 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.