基于目标相干性表征差异的多波段SAR相干变化检测方法

冀广宇; 董勇伟; 卜运成; 李焱磊; 周良将; 梁兴东

doi:10.12000/JR18020

基于目标相干性表征差异的多波段SAR相干变化检测方法

doi: 10.12000/JR18020

中国科学院电子学研究所微波成像技术重点实验室北京 100190
中国科学院大学北京 100049

基金项目: 国家部委基金

详细信息

作者简介:
冀广宇(1988–)，男，辽宁沈阳人，博士生。2011年于中国科学技术大学获得学士学位，现于中国科学院电子学研究所攻读博士学位。研究方向为机载合成孔径雷达信号处理。E-mail: gyji@mail.ustc.edu.cn

董勇伟(1982–)，男，湖北天门人，副研究员，硕士生导师。2009年于中国科学院电子学研究所获得博士学位，现担任中国科学院电子学研究所副研究员，中国科学院大学硕士生导师。主要研究方向为高分辨率小型化合成孔径雷达系统技术、雷达低慢小目标探测技术。E-mail: ywdong@mail.ie.ac.cn

卜运成(1991–)，男，湖南醴陵人，博士生。2013年于电子科技大学获得学士学位，现于中国科学院电子学研究所攻读博士学位。研究方向为多通道SAR三维成像、阵列干涉SAR系统定标、雷达信号处理。E-mail: buyuncheng@163.com

李焱磊(1983–)，男，河北定兴人，副研究员，硕士生导师。2013年于中国科学院电子学研究所获得博士学位，现担任中国科学院电子学研究所副研究员，中国科学院大学硕士生导师。主要研究方向为穿墙雷达成像技术。E-mail: yllee@mail.ie.ac.cn

周良将(1981–)，男，湖南新田人，副研究员，硕士生导师。2009年于中国科学院电子学研究所获得博士学位，现担任中国科学院电子学研究所副研究员，微波成像技术重点实验室副主任，中国科学院大学硕士生导师。主要研究方向为多维度合成孔径雷达系统技术。E-mail: ljzhou@mail.ie.ac.cn

梁兴东(1973–)，男，陕西西安人，研究员，博士生导师。2002年于北京理工大学获得博士学位，现为中国科学院电子学研究所研究员，中国科学院大学博士生导师，中国电子学会信号处理分会委员，《雷达学报》编委会委员。主要研究方向为阵列干涉SAR系统技术、微波光子成像雷达系统技术、片上雷达微系统技术。E-mail: xdliang@mail.ie.ac.cn

通讯作者:
梁兴东　 xdliang@mail.ie.ac.cn

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出版历程
- 收稿日期: 2018-03-07
- 修回日期: 2018-03-14
- 网络出版日期: 2018-08-28

Multi-band SAR Coherent Change Detection Method Based on Coherent Representation Differences of Targets

Key Laboratory of Science and Technology on Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Ministries Foundation

摘要

摘要: 相干变化检测(Coherent Change Detection, CCD)利用变化前后SAR图像间的相位相干性检测场景中发生的微小变化。传统的CCD方法由于对目标探测尺度单一，难以区分场景中目标变化区域与低相干干扰区域。多波段SAR对目标进行多尺度探测，依据电磁波对目标的穿透特性、目标的结构特性以及目标发生的变化尺度形成不同的相干性表征。该文据此提出一种多波段CCD方法。该方法先分别获取各个波段的相干变化差异图，然后依据目标的多波段相干性表征使用改进的期望最大化(Expectation-Maximization, EM)算法对场景分类，接下来根据少量监督样本确定目标变化类别，最后用Dempster-Shafer (DS)证据理论处理，获取多波段融合相干变化差异图。该结果可有效排除各个单波段存在的低相干干扰，达到降低虚警概率的目的。该文采用变化前后的L波段与P波段重轨SAR数据进行方法验证，实验结果与指标参数证明了该方法的有效性与正确性。
- 相干变化检测 /
- 多波段合成孔径雷达 /
- 相干性表征 /
- EM算法 /
- DS证据理论
Abstract: Coherent Change Detection (CCD) detects micro changes in a scene using phase coherence of SAR images before and after a change. It is difficult for conventional CCD method to distinguish low coherence interference region from objective change region because of a single detection scale. Multi-band SAR target detection in a multiscale way develops variable coherent representation according to the diversity of electromagnetic wave penetration, target structure, and change magnitude. In this paper, a multi-band CCD method is proposed. Firstly, the CCD images of every band were acquired; secondly, the detected scene was classified on the basis of coherent representation of targets in each single band using improved Expectation-Maximization (EM) algorithm; thirdly, the objective change class in each single band was selected by a few supervised samples; lastly, multi-band fusion CCD image was acquired by the use of Dempster-Shafer (DS) evidence theory. This multi-band CCD result can eliminate low coherent interference in each single band and decrease false alarm probability. The method is validated by L- and P-band repeat-pass SAR images acquired before and after change. Results and index parameters demonstrate the validity and correctness of the proposed method.
- Coherent Change Detection (CCD) /
- Multi-band Synthetic Aperture Radar (SAR) /
- Coherent representation /
- Expectation-Maximization (EM) algorithm /
- Dempster-Shafer (DS) evidence theory

HTML全文

图 1 多波段CCD方法流程图

Figure 1. Workflow of proposed multi-band CCD method

下载: 全尺寸图片幻灯片

图 2 待检测场景各波段变化前后SAR图像与光学参考图像

Figure 2. SAR images of each band of scene before and after change to be detected and optical reference image

下载: 全尺寸图片幻灯片

图 3 待检测场景相干变化差异图

Figure 3. CCD images of scene to be detected

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图 4 相干变化差异图的统计直方图及其拟合分布模型对比结果

Figure 4. Comparison results between histogram of CCD image and fitting distribution model

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图 5 样本监督数据直方图与拟合分布模型比较结果(为方便比较，将样本监督数据直方图乘以相应的倍数，使其与拟合分布分模型的概率密度大小相当)

Figure 5. Comparison results between histogram of supervised sample data and fitting distribution model(For the reason of comparing conveniently, the histogram of supervised sample data times a corresponding multiple, so that it can be equal to the probability density of some fitting model part)

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图 6 多波段CCD实验结果

Figure 6. Multi-band CCD result of experiment area

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图 7 单波段CCD与多波段CCD方法指标分析

Figure 7. Index analysis between mono-band CCD and multi-band CCD methods

下载: 全尺寸图片幻灯片

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