Synthetic aperture radar (SAR) images for automatic target classification (automatic target recognition (ATR)) have attracted significant interest as they can be acquired day and night under a wide range of weather conditions. However, SAR images can be time consuming to analyse, even for experts. ATR can alleviate this burden and deep learning is an attractive solution. A new deep learning Pose-informed architecture solution, that takes into account the impact of target orientation on the SAR image as the scatterers configuration changes, is proposed. The classification is achieved in two stages. First, the orientation of the target is determined using a Hough transform and a convolutional neural network (CNN). Then, classification is achieved with a CNN specifically trained on targets with similar orientations to the target under test. The networks are trained with translation and SAR-specific data augmentation. The proposed Pose-informed deep network architecture was successfully tested on the Military Ground Target Dataset (MGTD) and the Moving and Stationary Target Acquisition and Recognition (MSTAR) datasets. Results show the proposed solution outperformed standard AlexNets on the MGTD, MSTAR extended operating condition (EOC)1, EOC2 and standard operating condition (SOC)10 datasets with a score of 99.13% on the MSTAR SOC10.

中文翻译：

---

SAR ATR的姿态式深度学习方法

用于自动目标分类（自动目标识别（ATR））的合成孔径雷达（SAR）图像吸引了人们极大的兴趣，因为它们可以在各种天气条件下昼夜获取。但是，即使是专家，SAR图像的分析也可能很耗时。ATR可以减轻这种负担，深度学习是一种有吸引力的解决方案。提出了一种新的深度学习姿态通知架构解决方案，该解决方案考虑了随着散射体配置的变化，目标方向对SAR图像的影响。分类分两个阶段完成。首先，使用霍夫变换和卷积神经网络（CNN）确定目标的方向。然后，使用经过专门训练的CNN对目标进行分类，该目标具有与被测目标相似的方向。对网络进行了翻译和SAR专用数据增强培训。在军事地面目标数据集（MGTD）和移动和固定目标获取与识别（MSTAR）数据集上，成功测试了建议的基于姿态的深度网络体系结构。结果表明，所提出的解决方案在MGTD，MSTAR扩展操作条件（EOC）1，EOC2和标准操作条件（SOC）10数据集上的表现优于标准AlexNets，在MSTAR SOC10上的得分为99.13％。