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Description
In this paper, we describe compression strategies currently under consideration in the H2020 EO-ALERT project. In particular, we investigate the performance of the CCSDS-123.0-B Issue 2 standard for image compression when used for the purpose of compression of synthetic aperture radar (SAR) raw data onboard of satellite systems.
The task of compressing SAR raw data presents a great challenge compared to the compression of optical images as this kind of data consists of complex samples with low correlation among each other. Furthermore, the compression algorithm must have a low complexity due to the hardware constraints of the satellite systems.
Historically the most used algorithm and the de-facto standard in the field of raw SAR compression is the BAQ algorithm [1]. This algorithm is based on the assumption that the data can be modeled as a complex random process, where the imaginary and real parts are independent Gaussian samples with a slowly varying standard deviation. The technique consists of partitioning the data into blocks, over which the process can be assumed stationary, followed by the quantization of the data inside the blocks using a Max-Lloyd quantizer. Several versions have been proposed such as Entropy Constrained Block Adaptive Quantization [2], Block Adaptive Vector Quantization [3], Flexible Block Adaptive Quantization [4] which improve upon the performances of BAQ at the price of increased complexity.
These techniques only take advantage of the first-order statistics of the raw data, however in the past some approaches have been proposed, which try to exploit the correlation between the SAR raw data samples. Among such approaches there is the possibility to apply the concept of transform coding, for example using the Fourier Transform , Discrete Cosine Transform [5] or Wavelets [6], but usually these approaches are not adopted as they are too computationally complex.
The Standard CCSDS 123.0 “Low-Complexity Lossless & Near-Lossless Multispectral & Hyperspectral Image Compression” describes an algorithm for the compression of multispectral images on-board of satellites, and it is based on a DPCM-scheme followed by an entropic coder.
As the viability of this kind of algorithms for the purpose of compression of SAR captures was already acknowledged in papers such as [7], we tested the performances of this standard on SAR raw data in terms of rate and distortion.
We compressed the real part and the imaginary part of the SAR raw data separately using this standard and the obtained performance is equal to or better than that obtained by the BAQ technique on a dataset of images on real-worlds scene captured by the SIR-C/X-SAR mission [8].
The viability of this algorithm for SAR raw data compression is very advantageous as on satellites which capture both optical images and SAR data it would be possible to use the same algorithm to compress both types of data, instead of having to implement two different techniques.
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