论文标题
Despecknet：概括基于深度学习的SAR形象
deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling
论文作者
论文摘要
事实证明，深度学习（DL）是伪装合成孔径雷达（SAR）图像的合适方法。到目前为止，大多数DL模型都经过训练，以减少使用模拟噪声或特定的真实SAR图像的特定分布的斑点，从而限制了这些方法对具有未知噪声统计信息的真实SAR图像的适用性。在本文中，我们提出了一种DL方法DespeckNet1，该方法同时估算了斑点噪声分布和伪造的图像。由于它不取决于特定的噪声模型，因此DespeckNet在各种土地覆盖条件下跨采集中的跨收购都很好地概括了。我们评估了Despecknet在印度尼西亚，刚果民主共和国和荷兰获得的单个两极分化Sentinel-1图像上的性能，日本获得的单个极化ALOS-2/PALSAR-2图像以及在德国获得的Iceye X2图像。在所有情况下，Despecknet都能够有效减少斑点和还原
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
