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arXiv · 2012.03066

deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling

Adugna G. Mullissa·Diego Marcos·Devis Tuia·Martin Herold·Johannes Reiche
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Abstract

Deep learning (DL) has proven to be a suitable approach for despeckling synthetic aperture radar (SAR) images. So far, most DL models are trained to reduce speckle that follows a particular distribution, either using simulated noise or a specific set of real SAR images, limiting the applicability of these methods for real SAR images with unknown noise statistics. In this paper, we present a DL method, deSpeckNet1, that estimates the speckle noise distribution and the despeckled image simultaneously. Since it does not depend on a specific noise model, deSpeckNet generalizes well across SAR acquisitions in a variety of landcover conditions. We evaluated the performance of deSpeckNet on single polarized Sentinel-1 images acquired in Indonesia, The Democratic Republic of Congo and The Netherlands, a single polarized ALOS-2/PALSAR-2 image acquired in Japan and an Iceye X2 image acquired in Germany. In all cases, deSpeckNet was able to effectively reduce speckle and restore

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Adugna G. Mullissa, Diego Marcos, Devis Tuia, Martin Herold, Johannes Reiche. 2020-12-05. deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling. https://doi.org/10.1109/tgrs.2020.3042694

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