Reducing speckle and limiting the variations of the physical parameters in Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the potential of such data. Nowadays, deep learning approaches produce state of the art results in single-image SAR restoration. Nevertheless, huge multi-temporal stacks are now often available and could be efficiently exploited to further improve image quality. This paper explores two fast strategies employing a single-image despeckling algorithm, namely SAR2SAR, in a multi-temporal framework. The first one is based on Quegan filter and replaces the local reflectivity pre-estimation by SAR2SAR. The second one uses SAR2SAR to suppress speckle from a ratio image encoding the multi-temporal information under the form of a "super-image", i.e. the temporal arithmetic mean of a time series. Experimental results on Sentinel-1 GRD data show that these two multi-temporal strategies provide improved filtering results while adding a limited computational cost.
翻译:减少合成孔径雷达(SAR)图像中的光谱和限制物理参数的变异往往是充分利用这些数据潜力的关键步骤。如今,深层学习方法在单一图像合成孔径雷达的恢复方面产生最新的结果,然而,现在经常有巨大的多时堆叠,可以有效地加以利用,以进一步提高图像质量。本文探讨了在多时框架内采用单一图像脱光算法(SAR2SAR)的两种快速战略。第一个战略以Quegan过滤器为基础,取代SAR2SAR的当地反射预估值。第二个战略利用SAR2SAR从比例图像中将多时空信息编码为“超图像”的形式,即时间序列的时间算值值。Sentinel-1 GRD数据的实验结果表明,这两种多时空战略提供了改进的过滤结果,同时增加了有限的计算成本。