Dynamic Predictive Quantization for Staggered SAR: Experiments with Real SAR Data

Abstract:
For present and future spaceborne SAR missions, an increasing amount of onboard data is going to be demanded, due to the employment of large bandwidths, multiple polarizations, and large swath widths, which leads to hard requirements in terms of onboard memory and downlink capacity. In this context, SAR raw data quantization represents an essential aspect, since it affects both, the amount of data to be stored and transmitted to the ground and the quality of the resulting SAR products. Dynamic Predictive Block-Adaptive Quantization (DP-BAQ) is a novel technique consisting of a low-complexity data compression method particularly suitable for staggered SAR systems. In such a case, DP-BAQ conveniently exploits the existing correlation among the azimuth raw data samples, allowing for a data rate reduction of up to 25% with respect to state-of-the-art quantization methods. In this paper, the potentials of DP-BAQ are tested and validated on real airborne SAR data. The experimental data set has been acquired at L band by the airborne F-SAR sensor from DLR over the Kaufbeuren area, in Southern Germany. For this, a dedicated resampling and filtering of the data has been implemented in order to simulate staggered SAR data acquisitions. The present analyses verify the effectiveness of DP-BAQ for efficient data volume reduction.