Synthetic aperture radar (SAR) image classification is one of the most important subjects in automatic target recognition. Therefore, identifying the correct class of targets has significant importance to take a decision. Recently, several deep learning techniques, especially the convolutional neural networks (CNNs), have improved the SAR images classification performance due to its powerful perspective of feature learning and reasoning. Yet, CNN's generally need a huge amount of data for training and do not accurately manage the transformations in the input data. These drawbacks are overcome using a relatively new deep learning approach called capsule networks (CapsNets). In this study, the authors propose a method that adapts and incorporates CapsNet for the SAR image classification problem and improve recognition accuracy through a dual convolution CapsNet framework. Results obtained while experimenting on the moving and stationary target acquisition and recognition data set prove the effectiveness and the robustness of the proposed framework. The proposed experimental results demonstrate the superiority of the employed method overcoming both CNNs and CapsNet separate methods in term of classification accuracy.

中文翻译：

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基于双卷积胶囊网络的SAR-ATR方法

合成孔径雷达（SAR）图像分类是自动目标识别中最重要的主题之一。因此，确定正确的目标类别对做出决定至关重要。近来，由于其强大的特征学习和推理能力，几种深度学习技术（尤其是卷积神经网络（CNN））提高了SAR图像分类性能。但是，CNN通常需要大量的数据来进行训练，并且不能准确地管理输入数据中的转换。使用称为胶囊网络（CapsNets）的相对较新的深度学习方法可以克服这些缺点。在这个研究中，作者提出了一种方法，该方法适用于CapsNet并将其合并到SAR图像分类问题中，并通过双重卷积CapsNet框架提高了识别精度。在对移动目标和静止目标获取和识别数据集进行实验时获得的结果证明了所提出框架的有效性和鲁棒性。提出的实验结果表明，在分类精度方面，所采用的方法优于CNN和CapsNet分离方法。