Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims at removing such noise, so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970's, and several model-based algorithms have been developed in the subsequent years. The field has received growing attention, sparkled by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This paper surveys the literature on deep learning methods applied to SAR despeckling, covering both the supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods with the objective to recognize the most promising research lines, to identify the factors that have limited the success of deep models, and to propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling.
翻译:合成孔径雷达(SAR)的图像受到空间相关和信号依赖的噪音的影响,这种噪音被称为闪烁,非常严重,可能妨碍图像的利用。脱光是一项重要任务,目的是消除这种噪音,以提高所有下游图像处理任务的准确性。第一种脱光方法可追溯到1970年代,随后几年又制定了若干基于模型的算法。这个领域受到越来越多的关注,因为有强大的深层学习模型,在图像处理中产生反向问题的极佳性能。本文调查了用于合成孔径雷达的深层学习方法的文献,涵盖受监督的和最新的自我监督方法。我们对现有方法进行了批判性分析,目的是承认最有希望的研究路线,确定限制深层模型成功的因素,并提出前进的方法,以充分利用对合成孔径雷达的深层学习潜力。