SAR Target Classification Based on Integration of ASC Parts Model and Deep Learning Algorithm

Automatic target recognition of synthetic aperture radar (SAR) images has been a vital issue in recent studies. The recognition methods can be divided into two main types: traditional machine learning methods and deep-learning-based methods. For most traditional machine learning methods, target features are extracted based on electromagnetic scattering characteristics which are interpretable and stable. However, the extraction process of effective recognition features is often complex and the computational efficiency is low. Compared with the traditional methods, the deep learning methods can directly learn the high-dimensional features of the target to obtain higher target recognition accuracy. However, these algorithms have poor generalization performance and are difficult to explain. In order to comprehensively consider the advantages of the two kinds of methods, this article proposes a novel method for SAR target classification based on integration parts model and deep learning algorithm. First, part convolution and modified bidirectional convolutional-recurrent network are used to extract local feature of target through parts model which is calculated based on attribute scattering centers. Then, modified all-convolutional networks are used to extract the global feature of the target. The final classification result is achieved through decision fusion of local and global features. Experimental results on the moving and stationary target acquisition and recognition show the superiority of the proposed method, especially under complex conditions. Besides, a brief analysis of target key parts with part occlusion method is given, which is helpful to the interpretability of the deep learning network.

Automated summary

This paper introduces two main methods for automatic target recognition of synthetic aperture radar (SAR) images - traditional machine learning and deep learning, and proposes a new target classification method that combines the advantages of both methods. Experimental results demonstrate the superiority of this method under complex conditions, and the analysis of target key parts with the part occlusion method contributes to the interpretability of the deep learning network.