How to handle spatial correlations in SAR despeckling? Resampling strategies and deep learning approaches

Konferenz: EUSAR 2021 - 13th European Conference on Synthetic Aperture Radar
29.03.2021 - 01.04.2021 in online

Tagungsband: EUSAR 2021

Seiten: 6Sprache: EnglischTyp: PDF

Dalsasso, Emanuele; Tupin, Florence (LTCI, Télécom Paris, Institut Polytechnique de Paris, France)
Denis, Loic (Univ. Lyon, UJM-Saint-Etienne, CNRS, Institut d Optique Graduate School, Laboratoire Hubert Curien UMR 5516, Saint-Etienne, France)

Speckle noise strongly affects Synthetic Aperture Radar (SAR) images, causing strong intensity fluctuations that make them difficult to analyze. Although many speckle reduction algorithms have been proposed, how to effectively deal with the spatial correlations of speckle remains an open question, especially in the most recent deep learning approaches. This paper tries to address this problem. Existing approaches to tackle the speckle correlations are described. Then, a standard training strategy for deep learning is proposed. Two models are trained and the increased robustness brought by including a Total Variation (TV) term in the loss function is analyzed on Sentinel-1 images.