基于复图像的稀疏SAR成像方法在高分三号数据上的验证

毕辉 张冰尘 洪文 吴一戎

毕辉, 张冰尘, 洪文, 等. 基于复图像的稀疏SAR成像方法在高分三号数据上的验证[J]. 雷达学报, 2020, 9(1): 123–130. doi: 10.12000/JR19092
引用本文: 毕辉, 张冰尘, 洪文, 等. 基于复图像的稀疏SAR成像方法在高分三号数据上的验证[J]. 雷达学报, 2020, 9(1): 123–130. doi: 10.12000/JR19092
BI Hui, ZHANG Bingchen, HONG Wen, et al. Verification of complex image based sparse SAR imaging method on GaoFen-3 dataset[J]. Journal of Radars, 2020, 9(1): 123–130. doi: 10.12000/JR19092
Citation: BI Hui, ZHANG Bingchen, HONG Wen, et al. Verification of complex image based sparse SAR imaging method on GaoFen-3 dataset[J]. Journal of Radars, 2020, 9(1): 123–130. doi: 10.12000/JR19092

基于复图像的稀疏SAR成像方法在高分三号数据上的验证

doi: 10.12000/JR19092
基金项目: 国家自然科学基金(61901213),江苏省自然科学基金(BK20190397),江苏省科协青年科技人才托举工程
详细信息
    作者简介:

    毕 辉(1991–),男,籍贯山东,博士,副研究员,硕士生导师。2017年在中国科学院大学获得博士学位,现担任南京航空航天大学副研究员。主要研究方向为稀疏微波成像、雷达信号处理、雷达成像等

    张冰尘(1973–),男,籍贯浙江,博士,研究员,博士生导师。2017年在中国科学院大学获得博士学位,现担任中国科学院空天信息研究院研究员。主要研究方向为微波遥感与雷达技术、稀疏信号处理等

    洪 文(1968–),女,籍贯上海,博士,研究员,博士生导师。1997年在北京航空航天大学获得博士学位,现担任中国科学院空天信息创新研究院研究员。主要研究方向为合成孔径雷达成像与系统及其应用、极化/极化干涉合成孔径雷达数据处理及应用、3维微波成像新概念新体制新方法等

    吴一戎(1963–),男,籍贯安徽,博士,中国科学院院士,研究员,博士生导师。2001年在中国科学院电子学研究所获得博士学位,现担任中国科学院空天信息创新研究院院长。主要研究方向为微波成像理论与技术、雷达信号处理与雷达系统等

    通讯作者:

    毕辉 bihui@nuaa.edu.cn

  • 中图分类号: TN957.5

Verification of Complex Image Based Sparse SAR Imaging Method on GaoFen-3 Dataset

Funds: The National Natural Science Foundation of China (61901213), The Natural Science Foundation of Jiangsu Province (BK20190397), The Young Science and Technology Talent Support Project of Jiangsu Science and Technology Association
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  • 摘要: 基于稀疏信号处理的合成孔径雷达(SAR)成像(稀疏SAR成像)是稀疏微波成像的一个重要研究方向,相较于经典SAR,稀疏SAR成像在提升成像性能等方面具有重要优势。然而,受困于较大计算代价,其难以用于大观测场景的稀疏恢复,这极大限制了其应用范围。此外,无论军用还是民用,各国星载SAR系统的技术性能指标均是保密的,因此相较于原始回波,通常的公开数据都是经匹配滤波算法重构的SAR复图像。因而如何基于复图像数据进行稀疏成像,对提升现有SAR图像质量、降低稀疏成像计算代价具有重要意义。高分三号是我国首颗1 m分辨率C波段多极化SAR卫星,它具有成像分辨率高、幅宽大等优势,对提升我国灾害监测、海洋监视等能力具有重要作用。该文将一种基于复图像数据的稀疏SAR成像技术引入到高分三号SAR复图像的性能提升当中。实验结果表明,经稀疏处理后的图像拥有更低的旁瓣、更高的信杂噪比以及更优的目标可分辨率能力。且类似于匹配滤波算法重建图像,稀疏恢复结果也可以很好地保持图像统计分布及相位信息,使得稀疏重构的高分三号SAR图像仍适用于干涉、恒虚警率检测等应用。

     

  • 图  1  不同方法的海岸线区域重建结果

    Figure  1.  The reconstructed images of coastal area by different methods

    图  2  不同方法的海面舰船重建结果

    Figure  2.  The reconstructed images of ships on the sea surface by different methods

    图  3  不同方法的城市区域重建结果

    Figure  3.  The reconstructed images of city by different methods

    图  4  稀疏SAR成像方法重构图像背景统计分布保持

    Figure  4.  Background distribution preservation of sparse SAR imaging method recovered image

    图  5  稀疏SAR成像方法重构图像相位信息保持

    Figure  5.  Phase information preservation of sparse SAR imaging method recovered image

    图  6  匹配滤波图像与本文稀疏SAR成像方法非稀疏解之间的相位差

    Figure  6.  Phase difference between the MF recovered image and the non-sparse solution of sparse SAR imaging method

    表  1  不同方法重建结果的目标背景比TBR(dB)

    Table  1.   TBR values of the recovered images by different methods (dB)

    区域/方法匹配滤波重构图像稀疏解非稀疏解
    区域130.283541.837941.8346
    区域234.249644.398144.3956
    区域339.994850.953750.9509
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出版历程
  • 收稿日期:  2019-10-15
  • 修回日期:  2020-01-12
  • 网络出版日期:  2020-02-28

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