SAR-ATR method based on dual convolution capsule network

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.