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

计量
- 文章访问数: 3344
- HTML全文浏览量: 957
- PDF下载量: 426
- 被引次数: 0
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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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)

下载: 全尺寸图片幻灯片

图 6 多波段CCD实验结果

Figure 6. Multi-band CCD result of experiment area

下载: 全尺寸图片幻灯片

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

参考文献(25)

[1]	Preiss M and Stacy N J S. Coherent change detection: Theoretical description and experimental results[R]. DSTO-TR-1851, 2006
[2]	Jung J, Kim D J, Lavalle M, et al. Coherent change detection using InSAR temporal decorrelation model: A case study for volcanic ash detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 5765–5775. DOI: 10.1109/TGRS.2016.2572166
[3]	Barber J and Kogon S. Probabilistic three-pass SAR coherent change detection[C]. Proceedings of 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, California, USA, 2012: 1723–1726. DOI: 10.1109/ACSSC.2012.6489327
[4]	Muhuri A, Ratha D, and Bhattacharya A. Seasonal snow cover change detection over the Indian Himalayas using polarimetric SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(12): 2340–2344. DOI: 10.1109/LGRS.2017.2764123
[5]	Wahl D E, Yocky D A, Jakowatz C V, et al. A new maximum-likelihood change estimator for two-pass SAR coherent change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4): 2460–2469. DOI: 10.1109/TGRS.2015.2502219
[6]	冷英, 李宁. 一种改进的变化检测方法及其在洪水监测中的应用[J]. 雷达学报, 2017, 6(2): 204–212. DOI: 10.12000/JR16139 Leng Ying and Li Ning. Improved change detection method for flood monitoring[J]. Journal of radars, 2017, 6(2): 204–212. DOI: 10.12000/JR16139
[7]	An L, Li M, Zhang P, et al. Discriminative random fields based on maximum entropy principle for semisupervised SAR image change detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(8): 3395–3404. DOI: 10.1109/JSTARS.2015.2483320
[8]	徐真, 王宇, 李宁, 等. 一种基于CNN的SAR图像变化检测方法[J]. 雷达学报, 2017, 6(5): 483–491. DOI: 10.12000/JR17075 Xu Zhen, Wang Robert, Li Ning et al. A novel approach to change detection in SAR images with CNN classification[J]. Journal of radars, 2017, 6(5): 483–491. DOI: 10.12000/JR17075
[9]	Nielsen A A, Conradsen K, and Skriver H. Change detection in full and dual polarization, single-and multifrequency SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 4041–4048. DOI: 10.1109/JSTARS.2015.2416434
[10]	Nielsen A A, Conradsen K, and Skriver H. Corrections to " change detection in full and dual polarization, single-and multi-frequency SAR data”[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(11): 5143–5144. DOI: 10.1109/JSTARS.2017.2761038
[11]	Reigber A, Jäger M, and Krogager E. Polarimetric SAR change detection in multiple frequency bands for environmental monitoring in Arctic regions[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016: 5702–5705. DOI: 10.1109/IGARSS.2016.7730489
[12]	Li Y L, Liang X D, Zhou L J, et al.. Introduction to IECAS-SAR — A multi-frequency polarimetric airborne SAR[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, Quebec, Canada, 2014: 1085–1088. DOI: 10.1109/IGARSS.2014.6946617
[13]	Jackson J A and Moses R L. Feature extraction algorithm for 3D scene modeling and visualization using monostatic SAR[C]. Proceedings of SPIE 6237 Algorithms for Synthetic Aperture Radar Imagery XIII, Orlando, 2006, 6237: 623708. DOI: 10.1117/12.666558
[14]	文贡坚, 朱国强, 殷红成, 等. 基于三维电磁散射参数化模型的SAR目标识别方法[J]. 雷达学报, 2017, 6(2): 115–135. DOI: 10.12000/JR17034 Wen Gongjian, Zhu Guoqiang, Yin Hongcheng, et al. SAR ATR based on 3D parametric electromagnetic scattering model[J]. Journal of radars, 2017, 6(2): 115–135. DOI: 10.12000/JR17034
[15]	袁孝康. 星载合成孔径雷达导论[M]. 北京: 国防工业出版社, 2003: 234–235 Yuan Xiao-kang. Introduce to the Spaceborne Sythetic Aperture Radar[M]. Beijing: National Defense Industry Press, 2003: 234–235
[16]	陈述彭, 童庆禧, 郭华东. 遥感信息机理研究[M]. 北京: 科学出版社, 1998: 274–288 Chen Shu-peng, Tong Qing-xi, and Guo Hua-dong. Mechanism of Remote Sensing Information[M]. Beijing: Science Press, 1998: 274–288
[17]	周智敏, 黄晓涛. VHF/UHF超宽带合成孔径雷达穿透性能分析[J]. 系统工程与电子技术, 2003, 25(11): 1336–1340. DOI: 10.3321/j.issn:1001-506X.2003.11.007 Zhou Zhi-min and Huang Xiao-tao. Penetration performance analysis of VHF/UHF ultra-wideband synthetic aperture radar[J]. Systems Engineering and Electronics, 2003, 25(11): 1336–1340. DOI: 10.3321/j.issn:1001-506X.2003.11.007
[18]	Yin Q, Li Y, Huang P P, et al. Analysis of InSAR coherence loss caused by soil moisture variation[J]. Journal of Radars, 2015, 4(6): 689–697. DOI: 10.12000/JR15075
[19]	黄培康, 殷红成, 许小剑. 雷达目标特性[M]. 北京: 电子工业出版社, 2005: 9–62 Huang Pei-kang, Yin Hong-cheng, and Xu Xiao-jian. Radar Target Characteristics[M]. Beijing: Publishing House of Electronics Industry, 2005: 9–62
[20]	冀广宇, 董勇伟, 李焱磊, 等. 一种基于概率图模型的多时相SAR相干变化检测方法[J]. 电子与信息学报, 2017, 39(12): 2912–2920. DOI: 10.11999/JEIT170208 Ji Guang-yu, Dong Yong-wei, Li Yan-lei, et al. A multi-temporal SAR coherent change detection method based on probabilistic graphical models[J]. Journal of Electronics&Information Technology, 2017, 39(12): 2912–2920. DOI: 10.11999/JEIT170208
[21]	尤红建, 付琨. 合成孔径雷达图像精准处理[M]. 北京: 科学出版社, 2011: 125–139 You Hong-jian and Fu Kun. Precise Processing of Synthetic Aperture Radar Images[M]. Beijing: Science Press, 2011: 125–139
[22]	Touzi R, Lopes A, Bruniquel J, et al. Coherence estimation for SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(1): 135–149. DOI: 10.1109/36.739146
[23]	Anfinsen S N and Eltoft T. Application of the matrix-variate mellin transform to analysis of polarimetric radar images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 2281–2295. DOI: 10.1109/TGRS.2010.2103945
[24]	杨祥立, 徐德伟, 黄平平, 等. 融合相干/非相干信息的高分辨率SAR图像变化检测[J]. 雷达学报, 2015, 4(5): 582–590. DOI: 10.12000/JR15073 Yang Xiang-li, Xu De-wei, Huang Ping-ping, et al. Change detection of high resolution SAR images by the fusion of coherent/incoherent information[J]. Journal of Radars, 2015, 4(5): 582–590. DOI: 10.12000/JR15073
[25]	王桂婷, 王幼亮, 焦李成. 基于快速EM算法和模糊融合的多波段遥感影像变化检测[J]. 红外与毫米波学报, 2010, 29(5): 383–388 Wang Gui-ting, Wang You-liang, and Jiao Li-cheng. Change detection method of multiband remote sensing images based on fast expectation-maximization algorithm and fuzzy fusion[J]. Journal of Infrared and Millimeter Waves, 2010, 29(5): 383–388