@inproceedings{bf598d3d8c5a4e9caf18216e3cf5b484,
title = "Fast Strategies for Multi-Temporal Speckle Reduction of Sentinel-1 GRD Images",
abstract = "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 [1], in a multi-temporal framework. The first one is based on Quegan filter [2] 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.",
keywords = "SAR imaging, deep learning, multi-temporal series, speckle reduction",
author = "Ines Meraoumia and Emanuele Dalsasso and Loic Denis and Florence Tupin",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",
year = "2022",
month = jan,
day = "1",
doi = "10.1109/IGARSS46834.2022.9883448",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "687--690",
booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",
}