Sentinel-1A: Analysis of FDBAQ Performance on Real Data

Abstract

S ENTINEL-1 (S-1) is an imaging synthetic aperture radar (SAR) mission at C-band designed to supply all-weather day-and-night imagery to a number of operational Earth observation-based services. The mission is composed of a constellation of satellites for Earth observation, which was developed within the Copernicus program [previously known as Global Monitoring for Environment and Security (GMES)], i.e., the European program for the establishment of a European capacity for Earth observation [1].The most relevant applications cover (but are not limited to) mapping in support of humanitarian aid in crisis situations, monitoring sea and ice zones (arctic, surveillance of marine Manuscript

Metrics to evaluate compression algorithms for raw SAR data

“…Not only are platforms becoming smaller, but SAR technology is moving towards being multi‐band, multi‐polarised, having very high resolution and also having multiple operating modes. All of these factors contribute to SAR systems producing an ever‐increasing volume of data, so despite technological advances, the on‐board storage and downlink bandwidth remain limiting factors [6, 7]. Due to the limited SWAP‐C of many platforms, minimal processing can be performed on board, and thus the data need to be transferred to a ground station for processing.…”

“…Due to the limited SWAP‐C of many platforms, minimal processing can be performed on board, and thus the data need to be transferred to a ground station for processing. In order to accommodate the downlink and storage limitations, the raw SAR data must be compressed [7]. The objective of the compression algorithm is thus to reduce the data produced by the SAR to as few bits as possible, while using only limited processing and preserving the information content.…”

“…The de facto standard is the BAQ [6, 19] which utilises a Lloyd‐Max quantiser [20, 21] to exploit the fact that the in‐phase (I) and quadrature (Q) components can be assumed to follow Gaussian distributions with zero mean [8, 9]. More advanced variations of this method have been implemented on new SAR systems as processing capabilities improve [7], but the underlying BAQ algorithm is still being used.…”

An Improved BAQ Encoding and Decoding Method for Improving the Quantized SNR of SAR Raw Data

“…Considering the complexity of the algorithm, compression time, hardware conditions, and other factors, the block adaptive quantization (BAQ) algorithm is mainly adopted in SAR satellites for its simplicity and efficiency. For example, it has been used in the American Magellan SAR [7], ESA’s Sentinel-1 [8], Canada’s Radarsat-2 [9], Germany’s TanDEM-X [10], Chinese GF-3 SAR [11], and so on. The BAQ algorithm can adaptively quantify the echo with variant power intensity, and get the best quantization SNR for the Gauss distributed echo.…”

“…To enhance the performance, and also to reduce the data rate, many improved BAQ methods have been proposed. For example, the flexible dynamic block adaptive quantizer algorithm used by Sentinel-1 is proposed in the literature [8], which is a data compression algorithm that automatically selects a BAQ quantizer out of a set of five quantizers, based on an estimation of the local SNR estimated blocks on the acquired data. In these two methods, the overall performances are improved under limited data rate, but the performance of the saturated data cannot be improved.…”