Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images
This work addresses image quality enhancement for remote sensing applications, but it is incremental as it builds on existing methods.
The paper tackles speckle reduction in multi-temporal SAR images by adapting a single-image despeckling algorithm (SAR2SAR) into two fast multi-temporal strategies, showing improved filtering results with limited computational cost on Sentinel-1 GRD data.
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.