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Metrics to evaluate compression algorithms for raw SAR data

机译:评估原始SAR数据压缩算法的指标

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Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP-C) limitations since platforms are becoming smaller while SAR operating modes are becoming more complex. Thus, SAR systems are producing an ever-increasing volume of data that needs to be transferred to a ground station for processing. A compression algorithm seeks to reduce the data volume of the raw data; however, the algorithm can cause degradation and losses that may degrade the effectiveness of the SAR mission. This work addresses the lack of standardised quantitative performance metrics so that the performance of SAR data-compression algorithms can be objectively quantified. Therefore, metrics are established in two different domains, namely the data domain and the image domain. Since different levels of degradation are acceptable for different SAR applications, a trade-off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP-C limitations, there remains a trade-off between the performance and the computational complexity of the compression algorithm.
机译:现代合成孔径雷达(SAR)系统具有尺寸,重量,功率和成本(SWAP-C)的局限性,因为平台变得越来越小,而SAR操作模式却越来越复杂。因此,SAR系统产生的数据量不断增加,需要将其传输到地面站进行处理。压缩算法试图减少原始数据的数据量。但是,该算法可能导致性能下降和损失,从而可能降低SAR任务的效率。这项工作解决了缺乏标准化的量化性能指标的问题,因此可以客观地量化SAR数据压缩算法的性能。因此,在两个不同的域中建立度量,即数据域和图像域。由于不同的SAR应用可接受不同程度的降级,因此可以在数据减少与算法导致的降级之间做出权衡。由于SWAP-C的局限性,压缩算法的性能和计算复杂性之间仍然需要权衡。

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