论文标题
降低Sentinel-1 GRD图像的多个时间斑点的快速策略
Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images
论文作者
论文摘要
减少斑点并限制合成孔径雷达（SAR）图像中物理参数的变化通常是充分利用此类数据潜力的关键。如今，深度学习方法产生了最新的现状，从而导致单位SAR修复。然而，现在经常可用巨大的多阶段堆栈，并且可以有效利用以进一步提高图像质量。本文探讨了两种快速的策略，这些策略采用单像伪装算法，即SAR2SAR，在多个阶段的框架中。第一个是基于Quegan过滤器，并取代了SAR2SAR的局部反射率预估计。第二个使用SAR2SAR来抑制从“超级图像”的形式（即时间序列的时间算术平均值）形式的形式编码多个时间段信息的比率图像中抑制斑点。 Sentinel-1 GRD数据的实验结果表明，这两种多时间策略提供了改进的过滤结果，同时增加了有限的计算成本。
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.
