基于对数域加性信号分解的时序SAR图像相干斑抑制方法

康健 童风雨 白雨松 丁翔 冀腾宇 张柘

康健, 童风雨, 白雨松, 等. 基于对数域加性信号分解的时序SAR图像相干斑抑制方法[J]. 雷达学报, 2023, 12(5): 1031–1043. doi: 10.12000/JR22242
引用本文: 康健, 童风雨, 白雨松, 等. 基于对数域加性信号分解的时序SAR图像相干斑抑制方法[J]. 雷达学报, 2023, 12(5): 1031–1043. doi: 10.12000/JR22242
KANG Jian, TONG Fengyu, BAI Yusong, et al. SAR time series despeckling based on additive signal component decomposition in logarithm domain[J]. Journal of Radars, 2023, 12(5): 1031–1043. doi: 10.12000/JR22242
Citation: KANG Jian, TONG Fengyu, BAI Yusong, et al. SAR time series despeckling based on additive signal component decomposition in logarithm domain[J]. Journal of Radars, 2023, 12(5): 1031–1043. doi: 10.12000/JR22242

基于对数域加性信号分解的时序SAR图像相干斑抑制方法

doi: 10.12000/JR22242
基金项目: 国家自然科学基金(62101371),江苏省青年基金(BK20210707)
详细信息
    作者简介:

    康 健,副教授,硕士生导师,主要研究方向为SAR/InSAR信号处理、遥感图像解译等

    童风雨,硕士生,主要研究方向为目标检测技术

    白雨松,硕士生,主要研究方向为SAR干涉技术

    丁 翔,硕士生,主要研究方向为SAR图像去噪

    冀腾宇,讲师,硕士生导师,主要研究方向为张量分解等

    张 柘,研究员,博士生导师,主要研究方向为稀疏信号处理、稀疏微波成像等

    通讯作者:

    康健 jiankang@suda.edu.cn

    张柘 zhangzhe01@aircas.ac.cn

  • 责任主编:陈溅来 Corresponding Editor: CHEN Jianlai
  • 中图分类号: TP753

SAR Time Series Despeckling Based on Additive Signal Component Decomposition in Logarithm Domain

Funds: The National Natural Science Foundation of China (62101371), Jiangsu Province Science Foundation for Youths (BK20210707)
More Information
  • 摘要: 随着合成孔径雷达(SAR)在测绘带宽度、空间以及时间分辨率上的大幅提升,由不同时间获取的SAR图像配准得到的时间序列能更加精确地提供观测区域的动态变化信息。然而,相干斑噪声以及沿时间维度突变信号为后续的解译工作带来了严重挑战。尽管现有的主流方法可以对时序SAR图像的相干斑进行有效抑制,但沿时间维度突变信号会对去噪结果产生干扰。为更好地解决此问题,该文提出了一种基于对数域加性信号分解的方法,能同时抑制相干斑噪声并且对时序图像中的稳定信号和沿时间维度突变信号进行分离,从而消除突变信号对于去噪结果的影响。在仿真数据受到突变信号干扰的情况下,所提方法与其他主流方法相比,其去噪结果在峰值信噪比(PSNR)指标上取得了大约3 dB的提升。在哨兵1号数据上,所提方法能鲁棒地对时序图像中的相干斑噪声进行抑制,并且得到的突变信号成分也为后续的解译工作提供了参考数据。

     

  • 图  1  含有沿时间维度突变信号的SAR幅度时间序列

    Figure  1.  SAR amplitude time series with outliers along the temporal dimension

    图  2  本文所提方法流程图

    Figure  2.  Flow chart of the proposed method

    图  3  本文采用的仿真数据

    Figure  3.  Simulation data used in this paper

    图  4  仿真数据相干斑抑制结果

    Figure  4.  Speckle suppression results of the simulated data

    图  5  本文方法的参数敏感性分析

    Figure  5.  Parameter sensitivity analyses of the proposed method

    图  6  所提方法对于图像数目的敏感性分析

    Figure  6.  Sensitivity analysis of the proposed method with respect to the number of images

    图  7  上海近海区域哨兵1号数据的相干斑抑制结果

    Figure  7.  Speckle suppression results for Sentinel-1 data in the Shanghai offshore region

    图  8  上海浦东机场区域哨兵1号数据的相干斑抑制结果

    Figure  8.  Speckle suppression results for Sentinel-1 data in Shanghai pudong airport region

    图  9  近海区域图像边缘提取结果对比

    Figure  9.  Comparison of image edge extraction results in offshore areas

    图  10  海上舰船区域图像边缘提取结果对比

    Figure  10.  Comparison of image edge extraction results of ship region

    表  1  仿真数据相干斑抑制结果的定量分析

    Table  1.   Quantitative analysis of speckle suppression results in simulation data

    方法PSNR (dB)MSSIM (dB)MCC
    0.5%1.0%0.5%1.0%0.5%1.0%
    SqueeSAR30.9529.8128.420.510.430.380.870.800.75
    MSAR-BM3D35.0118.0215.120.560.260.180.930.350.26
    RABASAR35.0732.9730.510.630.490.390.960.910.82
    DespecKS-NLLRTV29.5328.7028.410.540.310.280.900.880.88
    本文方法32.0732.8233.150.570.560.550.920.920.91
    下载: 导出CSV

    表  2  4块同质区域计算得到的等效视数

    Table  2.   The equivalent apparent number calculated by four homogeneous regions

    方法ENL
    A1A2A3A4
    原始图像0.8720.7940.9070.936
    SqueeSAR325.303.2564.3372.55
    MSAR-BM3D176.4015.4865.22117.07
    RABASAR26.3324.4024.1317.07
    DespecKS-NLLRTV54.8659.32110.7085.61
    本文方法107.3074.6880.11139.50
    下载: 导出CSV

    表  3  沿时间维度突变信号的熵值分析

    Table  3.   Entropy analysis of the outliers along the temporal dimension

    地点 测试日期DespecKS-NLLRTV本文方法
    近海2020-09-1514.6180.306
    2021-02-0615.7910.144
    机场2019-12-0218.6230.643
    2020-09-2716.0320.736
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-12-25
  • 修回日期:  2023-02-28
  • 网络出版日期:  2023-03-14
  • 刊出日期:  2023-10-28

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