基于子孔径与全孔径特征学习的SAR多通道虚假目标鉴别  

Multichannel False-target Discrimination in SAR Images Based on Sub-aperture and Full-aperture Feature Learning

作　　者：马琳 潘宗序 黄钟泠 韩冰 胡玉新[1,2,3] 周晓 雷斌[1,2,3] MA Lin;PAN Zongxu;HUANG Zhongling;HAN Bing;HU Yuxin;ZHOU Xiao;LEI Bin(Aerospace Information Research Institute,Chinese Academy of Science,Beijing 100190,China;Key Laboratory of Technology in Geo-spatial Information Processing and Application System,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院空天信息创新研究院,京100190 [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

出　　处：《雷达学报（中英文）》2021年第1期159-172,共14页Journal of Radars

基　　金：国家自然科学基金(61701478)。

摘　　要：SAR多通道引起的虚假目标与散焦的船舶目标形状纹理特征非常相似,在全孔径SAR图像中难以区分。针对此类虚假目标造成的虚警问题,该文提出一种基于子孔径与全孔径特征学习的SAR多通道虚假目标鉴别方法。首先,对复数SAR图像进行幅值计算得到幅度图像,利用迁移学习方法提取幅度图像中的全孔径特征;接着,对复数SAR图像进行子孔径分解获得一系列子孔径图像,然后用栈式卷积自编码器(SCAE)提取子孔径图像中的子孔径特征;最后,将子孔径和全孔径特征进行串联并利用联合特征进行分类。在高分三号超精细条带模式SAR图像上的实验结果表明,该方法可以有效的鉴别船舶目标和多通道虚假目标,与仅使用全孔径特征学习的方法相比准确率提升了16.32%。False targets caused by multichannel Synthetic Aperture Radar(SAR)are similar to a defocused ship in both shape and texture,making it difficult to discriminate in the full-aperture SAR image.To address the issue of false alarms caused by such false targets,this paper proposes a multichannel SAR false-target discrimination method based on sub-aperture and full-aperture feature learning.First,amplitude calculation is performed on complex SAR images to obtain the amplitude images,and transfer learning is utilized to extract the full-aperture features from the amplitude images.Then,sub-aperture decomposition is performed on complex SAR images to obtain a series of sub-aperture images,and the Stacked Convolutional Auto-Encoders(SCAE)are applied to extract the sub-aperture features from the sub-aperture images.Finally,the subaperture and the full-aperture features are concatenated to form the joint features,which are used to accomplish target discrimination.The accuracy of the method proposed in this paper is 16.32%higher than that of the approach only using the full-aperture feature on GF-3 UFS SAR images.

关 键 词：合成孔径雷达 深度学习 子孔径特征学习 船舶目标鉴别 多通道虚假目标 

分 类 号：TN958[电子电信—信号与信息处理] TP183[电子电信—信息与通信工程]