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.

中文翻译：

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通过新型SSF-Net模型的SAR图像目标识别方法。

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。根据SAR图像的特征，设计了一种稀疏数据特征提取模块（SDFE），并在此基础上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，网络采用三种方法对目标进行分类：三层全连接（FC）层，一层全连接（FC）层和全局平均池（GAP）。其中，后两种方法的参数和计算量较少，而且它们具有更好的实时性能。在公共数据集SAR-SOC和SAR-EOC-1上测试了这些方法。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上的识别精度最高，分别为99.55％和99.50％，比SAR-SOC的比较方法高1％。 EOC-1。