赵鹏菲^{1, 2, 3,},
黄丽佳^{1, 2,,}
1.中国科学院空天信息创新研究院 北京 100094
2.中国科学院空间信息处理与应用系统技术重点实验室 北京 100190
3.中国科学院大学 北京 100049
基金项目:国家自然科学基金(61991420, 62022082)，中科院青促会专项支持

详细信息

作者简介:赵鹏菲(1996–)，男，硕士生，研究方向为合成孔径雷达图像分析
黄丽佳(1984–)，女，博士，研究员，硕士生导师，研究方向为合成孔径雷达信号处理与图像分析

通讯作者:黄丽佳 iecas8huanglijia@163.com

责任主编：林赟 Corresponding Editor: LIN Yun
中图分类号:TP753

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出版历程

收稿日期:2020-10-26
修回日期:2020-12-21
网络出版日期:2021-01-07
刊出日期:2021-12-28

Target Recognition Method for Multi-aspect Synthetic Aperture Radar Images Based on EfficientNet and BiGRU

ZHAO Pengfei^{1, 2, 3,},
HUANG Lijia^{1, 2,,}
1. Aerospace Information Research Institutue, Chinese Academy of Sciences, Beijing 100094, China
2. Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
Funds:The National Natural Science Foundation of China (61991420, 62022082), Special Support of Youth Innovation Promotion Association Chinese Academy of Sciences

More Information

Corresponding author:HUANG Lijia, iecas8huanglijia@163.com

摘要
摘要:合成孔径雷达(SAR)的自动目标识别(ATR)技术目前已广泛应用于军事和民用领域。SAR图像对成像的方位角极其敏感，同一目标在不同方位角下的SAR图像存在一定差异，而多方位角的SAR图像序列蕴含着更加丰富的分类识别信息。因此，该文提出一种基于EfficientNet和BiGRU的多角度SAR目标识别模型，并使用孤岛损失来训练模型。该方法在MSTAR数据集10类目标识别任务中可以达到100%的识别准确率，对大俯仰角(擦地角)下成像、存在版本变体、存在配置变体的3种特殊情况下的SAR目标分别达到了99.68%, 99.95%, 99.91%的识别准确率。此外，该方法在小规模的数据集上也能达到令人满意的识别准确率。实验结果表明，该方法在MSTAR的大部分数据集上识别准确率均优于其他多角度SAR目标识别方法，且具有一定的鲁棒性。
关键词:合成孔径雷达/
自动目标识别/
多角度识别/
EfficientNet
Abstract:Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) has been extensively applied in military and civilian fields. However, SAR images are very sensitive to the azimuth of the images, as the same target can differ greatly from different aspects. This means that more reliable and robust multiaspect ATR recognition is required. In this paper, we propose a multiaspect ATR model based on EfficientNet and BiGRU. To train this model, we use island loss, which is more suitable for SAR ATR. Experimental results have revealed that our proposed method can achieve 100% accuracy for 10-class recognition on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. The SAR targets in three special imaging cases with large depression angles, version variants, and configuration variants reached recognition accuracies of 99.68%, 99.95%, and 99.91%, respectively. In addition, the proposed method achieves satisfactory accuracy even with smaller datasets. Our experimental results show that our proposed method outperforms other state-of-the-art ATR methods on most MSTAR datasets and exhibits a certain degree of robustness.
Key words:Synthetic Aperture Radar (SAR)/
Automatic Target Recognition (ATR)/
Multi-aspect SAR/
EfficientNet



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