  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
