Wang Wei, Zhang Chengwen, Tian Jinge, Ou Jianping, Li Ji
School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.
ATR Key Lab., National University of Defense Technology, Changsha 410073, China.
Comput Intell Neurosci. 2020 Jul 9;2020:8859172. doi: 10.1155/2020/8859172. eCollection 2020.
With the wide application of high-resolution radar, the application of Radar Automatic Target Recognition (RATR) is increasingly focused on how to quickly and accurately distinguish high-resolution radar targets. Therefore, Synthetic Aperture Radar (SAR) image recognition technology has become one of the research hotspots in this field. Based on the characteristics of SAR images, a Sparse Data Feature Extraction module (SDFE) has been designed, and a new convolutional neural network SSF-Net has been further proposed based on the SDFE module. Meanwhile, in order to improve processing efficiency, the network adopts three methods to classify targets: three Fully Connected (FC) layers, one Fully Connected (FC) layer, and Global Average Pooling (GAP). Among them, the latter two methods have less parameters and computational cost, and they have better real-time performance. The methods were tested on public datasets SAR-SOC and SAR-EOC-1. The experimental results show that the SSF-Net has relatively better robustness and achieves the highest recognition accuracy of 99.55% and 99.50% on SAR-SOC and SAR-EOC-1, respectively, which is 1% higher than the comparison methods on SAR-EOC-1.
随着高分辨率雷达的广泛应用，雷达自动目标识别（RATR）的应用越来越集中于如何快速、准确地区分高分辨率雷达目标。因此，合成孔径雷达（SAR）图像识别技术已成为该领域的研究热点之一。基于SAR图像的特点，设计了一种稀疏数据特征提取模块（SDFE），并基于SDFE模块进一步提出了一种新的卷积神经网络SSF-Net。同时，为了提高处理效率，该网络采用三种方法对目标进行分类：三个全连接（FC）层、一个全连接（FC）层和全局平均池化（GAP）。其中，后两种方法参数较少、计算成本较低，具有较好的实时性能。这些方法在公共数据集SAR-SOC和SAR-EOC-1上进行了测试。实验结果表明，SSF-Net具有相对较好的鲁棒性，在SAR-SOC和SAR-EOC-1上分别达到了99.55%和99.50%的最高识别准确率，比在SAR-EOC-1上的对比方法高出1%。
