Synthetic Aperture Radar (SAR) despeckling is an important problem in remote sensing as speckle degrades SAR images, affecting downstream tasks like detection and segmentation. Recent studies show that convolutional neural networks(CNNs) outperform classical despeckling methods. Traditional CNNs try to increase the receptive field size as the network goes deeper, thus extracting global features. However,speckle is relatively small, and increasing receptive field does not help in extracting speckle features. This study employs an overcomplete CNN architecture to focus on learning low-level features by restricting the receptive field. The proposed network consists of an overcomplete branch to focus on the local structures and an undercomplete branch that focuses on the global structures. We show that the proposed network improves despeckling performance compared to recent despeckling methods on synthetic and real SAR images.
翻译:最近的研究表明,进化神经网络(CNNs)优于古老的脱光方法。传统的CNN试图随着网络的更深,增加可接受场的规模,从而提取全球特征。然而,分光相对小,而接受场的增加无助于提取分光特征。本研究使用一个过于完整的CNN结构,通过限制可接受场来侧重于学习低水平特征。拟议的网络包括一个侧重于本地结构的超完整分支和一个侧重于全球结构的不完整分支。我们表明,拟议的网络与合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成合成图像的最近脱光化方法相比,提高了脱光性功能。