| 基于FCNN和ICAE的SAR图像目标识别方法 |
| |
| 引用本文： | 喻玲娟,王亚东,谢晓春,林赟,洪文.基于FCNN和ICAE的SAR图像目标识别方法[J].雷达学报,2018,7(5):622-631. |
| |
| 作者姓名： | 喻玲娟 王亚东 谢晓春 林赟 洪文 |
| |
| 基金项目： | 国家自然科学基金（61431018，61501210，61571421），江西省自然科学基金（20161BAB202054），江西省教育厅科技项目（GJJ150684，GJJ170825） |
| |
| 摘 要： | 近年来，基于卷积神经网络(Convolutional Neural Network, CNN)的合成孔径雷达(Synthetic Aperture Radar, SAR)图像目标识别得到深入研究。全卷积神经网络(Fully Convolutional Neural Network, FCNN)是CNN结构上的改进，它比CNN能获得更高的识别率，但在训练过程中仍需要大量的带标签训练样本。该文提出一种基于FCNN和改进的卷积自编码器(Improved Convolutional Auto-Encoder, ICAE)的SAR图像目标识别方法，即先用ICAE无监督训练方式获得的编码器网络参数初始化FCNN的部分参数，后用带标签训练样本对FCNN进行训练。基于MSTAR数据集的十类目标分类实验结果表明，在不扩充带标签训练样本的情况下，该方法不仅能获得98.14%的平均正确识别率，而且具有较强的抗噪声能力。
|
| 关 键 词： | 合成孔径雷达 自动目标识别 全卷积神经网络 卷积自编码器 改进的卷积自编码器 |
| 收稿时间： | 2018-08-31 |
SAR ATR Based on FCNN and ICAE |
| |
| Yu Lingjuan,Wang Yadong,Xie Xiaochun,Lin Yun,Hong Wen.SAR ATR Based on FCNN and ICAE[J].Journal of Radars,2018,7(5):622-631. |
| |
| Authors: | Yu Lingjuan Wang Yadong Xie Xiaochun Lin Yun Hong Wen |
| |
| Affiliation: | ①.School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China②.Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China③.School of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China |
| |
| Abstract: | In recent years, Synthetic Aperture Radar (SAR) image target recognition based on the Convolutional Neural Network (CNN) has attracted a significant amount of attention. Fully CNN (FCNN) is a structural improvement of the CNN, which features a higher recognition rate than CNN, but it still requires a large number of labeled data in the training process. This study aims to propose a method of SAR image target recognition based on FCNN and Improved Convolutional Auto-Encoder (ICAE), where several parameters of FCNN are initialized by the parameters of the ICAE encoder. These parameters are obtained in the unsupervised training mode. Then, the FCNN is trained by the labeled training samples. The experimental results on 10 kinds of target classification based on the MSTAR datasets show that this method cannot only achieve an average of 98.14% correct recognition rate but also feature a strong anti-noise capability when the labeled training samples are unexpanded. |
| |
| Keywords: | |
|
| 点击此处可从《雷达学报》浏览原始摘要信息 |
|
点击此处可从《雷达学报》下载全文 |
|
