一种基于概率图模型的多时相SAR相干变化检测方法

冀广宇; 董勇伟; 李焱磊; 梁兴东

doi:10.11999/JEIT170208

一种基于概率图模型的多时相SAR相干变化检测方法

doi: 10.11999/JEIT170208

2.
(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) ②(中国科学院大学北京 100049)

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出版历程
- 收稿日期: 2017-03-07
- 修回日期: 2017-05-24
- 刊出日期: 2017-12-19

A Multi-temporal SAR Coherent Change Detection Method Based on Probabilistic Graphical Models

2.
(Key Laboratory of Science and Technology on Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)

摘要

摘要: 相干变化检测(CCD)利用重轨SAR数据对场景中表现为低相干特性的变化区域具有良好的检测性能，然而场景中诸如植被、阴影、强散射旁瓣、低散射等区域也呈现低相干特性，对检测结果造成干扰，尤其在高波段SAR CCD中，对检测效果影响更加明显。该文利用多时相SAR数据形成的相干变化差异图像(CCD图像)建立概率图模型，提出一种多时相CCD处理方法。该方法以多时相CCD图像作为观测量，通过选取合适的参与处理图像数量及优化场景中变化区域的分类，计算目标变化区域的后验概率，可有效减小低相干干扰区域造成的影响。仿真和实测数据结果验证了该方法的正确性和有效性。
- 重轨合成孔径雷达 /
- 相干变化检测 /
- 多时相 /
- 概率图模型 /
- 后验概率
Abstract: Coherent Change Detection (CCD) has good performance on detecting change regions with low coherence in the scene by using repeat-pass Synthetic Aperture Radar (SAR) data. However, some regions as vegetation, radar shadows, sidelobes of strong reflectivity and low reflectivity areas show low coherent character as well, which disturbs the result of change detection, especially in high frequency band SAR CCD with more evident disturbance. This paper proposes a multi-temporal CCD method by establishing a probabilistic graphical model using CCD images formed by multi-temporal SAR data. In this method, multi-temporal CCD images are used as observations to calculate a posterior probability of objective change region via choosing appropriate number of processing CCD images and optimizing the classification of change regions in the scene. The proposed method can reduce the disturbance of low coherence disturb regions effectively. The simulated and experimental results demonstrate the validity and effectiveness of the proposed method.
- Repeat-pass SAR /
- Coherent Change Detection (CCD) /
- Multi-temporal /
- Probabilistic graphical models /
- Posterior probability

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参考文献(23)

LIAO Mingsheng, JIANG Liming, LIN Hui, et al. Urban change detection based on coherence and intensity characteristics of SAR imagery[J]. Photogrammetric Engineering Remote Sensing, 2008, 74(8): 999-1006. doi: 10.14358/PERS.74.8.999.

PREISS M and STACY N J S. Coherent change detection: Theoretical description and experimental results[R]. DSTO- TR-1851, 2006.

JOHNSEN T. Coherent change detection in SAR images of harbors with emphasis on findings from container backscattering[C]. IEEE National Radar Conference, Kansas City, Missouri, USA, 2011: 118-123. doi: 10.1109/RADAR. 2011.5960512.

JUNG J, KIM D, 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.

YIN Qiang, LI Yang, HUANG Pingping, 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.

NEWEY M, BENITZ G, and KOGON S. A generalized likelihood ratio test for SAR CCD[C]. Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, California, USA, 2012: 1727-1730. doi: 10.1109/ ACSSC.2012.6489328.

赵军香, 梁兴东, 李焱磊. 一种基于似然比统计量的SAR相干变化检测[J]. 雷达学报, 2017, 6(2): 186-194. doi: 10.12000/ JR16065.

ZHAO Junxiang, LIANG Xingdong, and LI Yanlei. Change detection in SAR CCD based on the likelihood change Statistics[J]. Journal of Radars, 2017, 6(2): 186-194. doi: 10.12000/JR16065.

CHA M, PHILLIPS R D, and WOLFE P J. Test statistics for synthetic aperture radar coherent change detection[C]. IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, Michigan, USA, 2012: 856-859. doi: 10.1109/SSP.2012. 6319841.

CHA M, PHILLIPS R D, WOLFE P J, et al. Two-stage change detection for synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(12): 6547-6560. doi: 10.1109/TGRS.2015.2444092.

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 Xiangli, XU Dewei, HUANG Pingping, 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.

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.

SCARBOROUGH S M, GORHAM L, MINARDI M J, et al. A challenge problem for SAR change detection and data compression[J]. SPIE Proceedings, 2010, 7699: 1-5. doi: 10. 1117/12.855378.

AN Lin, LI Ming, ZHANG Peng, 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.

ZHOU Licun, CAO Guo, LI Yupeng, et al. Change detection based on conditional random field with region connection constraints in high-resolution remote sensing images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(8): 3478-3488. doi: 10.1109/ JSTARS.2016.2514610.

BARBER J and KOGON S. Probabilistic three-pass SAR coherent change detection[C]. Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, California, USA, 2012: 1723-1726. doi: 10.1109/ ACSSC.2012.6489327.

刘云龙, 周良将, 李焱磊, 等. 一种改进的机载SAR二维空变辐射校准方法[J]. 国外电子测量技术, 2016, 35(8): 9-14. doi: 10.3969/j.issn.1002-8978.2016.08.003.

LIU Yunlong, ZHOU Liangjiang, LI Yanlei, et al. Upgrade 2-D azimuth-variant radiometric calibration method for airborne SAR[J]. Foreign Electronic Measurement Technology, 2016, 35(8): 9-14. doi: 10.3969/j.issn.1002-8978.2016.08.003.

邓袁. 机载重轨干涉SAR高精度配准算法研究[D]. [硕士论文], 中国科学院大学, 2014: 41-54.

DENG Yuan. Research on highly precise registration algorithm of airborne repeat-pass interferometric SAR[D]. [Master dissertation], University of Chinese Academy of Sciences, 2014: 41-54.

KOLLER D and FRIEDMAN N. Probabilistic Graphical Models: Principles and Techniques[M]. Cambridge, Massachusetts, USA London, England, The MIT Press, 2009: 45-102.

TOIZI 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.

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