Volume 9 Issue 1
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Li Wan-chun, Huang Cheng-feng. Optimal Trajectory Analysis for the Receiver of Passive Location Systems Using Direction Of Arrival and Doppler Measurements[J]. Journal of Radars, 2014, 3(6): 660-665. doi: 10.12000/JR14118
Citation: XU Lu, ZHANG Hong, WANG Chao, et al. Progress in the processing and application of compact polarimetric SAR[J]. Journal of Radars, 2020, 9(1): 55–72. doi: 10.12000/JR19106
shu

Progress in the Processing and Application of Compact Polarimetric SAR

DOI: 10.12000/JR19106
  • 1.

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Beijing 100094, China

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

Funds:  The Natural National Science Foundation of China (41971395, 41930110)
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  • Corresponding author: ZHANG Hong, zhanghong@radi.ac.cn
  • Received Date: 2019-12-02
  • Rev Recd Date: 2020-02-02
    • Available Online: 2020-02-17
  • Publish Date: 2020-02-28
    Abstract
  • Polarimetric information enriches the content of a Synthetic Aperture Radar (SAR) and has been widely used in agriculture, environment, ocean, forest, military, and other fields. However, it also faces limitations regarding its low resolution and small width, which lead to high application cost. As a novel polarimetric SAR system that can simultaneously obtain relatively rich scatter information and large swath, Compact Polarimetric SAR (CP SAR) has attracted extensive attention from researchers in the past decade. With the successful launch of India’s RISAT-1 satellite, new progresses have been made in the application fields on CP SAR. In this paper, the classical data processing methods of CP SAR are briefly introduced and the main research results of the application of CP SAR in the agriculture and maritime fields over the past 10 years are summarized. Finally, the prospects on its development are given.

     

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    • References(117)
  • [1]
    LEE J S and POTTIER E. Polarimetric Radar Imaging: From Basics to Applications[M]. New York: CRC Press, 2009: 43–44.
    [2]
    LARRAÑAGA A and ÁLVAREZ-MOZOS J. On the added value of quad-pol data in a multi-temporal crop classification framework based on RADARSAT-2 imagery[J]. Remote Sensing, 2016, 8(4): 335. doi: 10.3390/rs8040335
    [3]
    WU Fu, WANG Chao, ZHANG Hong, et al. Rice crop monitoring in South China with RADARSAT-2 quad-polarization SAR data[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(2): 196–200. doi: 10.1109/LGRS.2010.2055830
    [4]
    YAJIMA Y, YAMAGUCHI Y, SATO R, et al. POLSAR image analysis of wetlands using a modified four-component scattering power decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(6): 1667–1673. doi: 10.1109/tgrs.2008.916326
    [5]
    ZHANG Biao, PERRIE W, LI Xiaofeng, et al. Mapping sea surface oil slicks using RADARSAT‐2 quad‐polarization SAR image[J]. Geophysical Research Letters, 2011, 38(10): L10602. doi: 10.1029/2011gl047013
    [6]
    NUNZIATA F, MIGLIACCIO M, and BROWN C E. Reflection symmetry for polarimetric observation of man-made metallic targets at sea[J]. IEEE Journal of Oceanic Engineering, 2012, 37(3): 384–394. doi: 10.1109/JOE.2012.2198931
    [7]
    洪文. 基于混合极化架构的极化SAR: 原理与应用(中英文)[J]. 雷达学报, 2016, 5(6): 559–595. doi: 10.12000/JR16074

    HONG Wen. Hybrid-polarity architecture based polarimetric SAR: Principles and applications[J]. Journal of Radars, 2016, 5(6): 559–595. doi: 10.12000/JR16074
    [8]
    SOUYRIS J C, IMBO P, FJORTOFT R, et al. Compact polarimetry based on symmetry properties of geophysical media: The π/4 mode[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 634–646. doi: 10.1109/TGRS.2004.842486
    [9]
    STACY N and PREISS M. Compact polarimetric analysis of X-band SAR data[C]. The 6th European Conference on Synthetic Aperture Radar, Dresden, Germany, 2006.
    [10]
    RANEY R K. Hybrid-polarity SAR architecture[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(11): 3397–3404. doi: 10.1109/TGRS.2007.895883
    [11]
    RANEY R K, CAHILL J T S, PATTERSON G W, et al. The m-chi decomposition of hybrid dual-polarimetric radar data with application to lunar craters[J]. Journal of Geophysical Research: Planets, 2012, 117(E12): E00H21. doi: 10.1029/2011je003986
    [12]
    RANEY R K, SPUDIS P D, BUSSEY B, et al. The lunar mini-RF radars: Hybrid polarimetric architecture and initial results[J]. Proceedings of the IEEE, 2011, 99(5): 808–823. doi: 10.1109/JPROC.2010.2084970
    [13]
    MISRA T and KUMAR A S K. Scatterometer and RISAT-1: ISRO’S contribution to radar remote sensing[C]. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 2015: 4220–4223. doi: 10.1109/IGARSS.2015.7326757.
    [14]
    YOKOTA Y, NAKAMURA S, ENDO J, et al. Evaluation of compact polarimetry and along track interferometry as experimental mode of PALSAR-2[C]. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 2015: 4125–4128. doi: 10.1109/IGARSS.2015.7326733.
    [15]
    SPACEX. RADARSAT constellation mission[EB/OL]. https://www.spacex.com/sites/spacex/files/radarsat_constellation_mission_press_kit.pdf, 2019.
    [16]
    RANEY R K. DESDynI adopts hybrid polarity SAR architecture[C]. 2009 IEEE Radar Conference, Pasadena, US, 2009: 1–4. doi: 10.1109/RADAR.2009.4977046.
    [17]
    PUTREVU D, DAS A, VACHHANI J G, et al. Chandrayaan-2 dual-frequency SAR: Further investigation into lunar water and regolith[J]. Advances in Space Research, 2016, 57(2): 627–646. doi: 10.1016/J.ASR.2015.10.029
    [18]
    张红, 谢镭, 王超, 等. 简缩极化SAR数据信息提取与应用[J]. 中国图象图形学报, 2013, 18(9): 1065–1073. doi: 10.11834/jig.20130902

    ZHANG Hong, XIE Lei, WANG Chao, et al. Information extraction and application of compact polarimetric SAR data[J]. Journal of Image and Graphics, 2013, 18(9): 1065–1073. doi: 10.11834/jig.20130902
    [19]
    NORD M E, AINSWORTH T L, LEE J S, et al. Comparison of compact polarimetric synthetic aperture radar modes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(1): 174–188. doi: 10.1109/TGRS.2008.2000925
    [20]
    YIN Junjun, YANG Jian, and ZHANG Xinzheng. On the ship detection performance with compact polarimetry[C]. 2011 IEEE RadarCon (RADAR), Kansas City, USA, 2011: 675–680. doi: 10.1109/RADAR.2011.5960623.
    [21]
    DENBINA M and COLLINS M J. Iceberg detection using compact polarimetric synthetic aperture radar[J]. Atmosphere-Ocean, 2012, 50(4): 437–446. doi: 10.1080/07055900.2012.733307
    [22]
    COLLINS M J, DENBINA M, and ATTEIA G. On the reconstruction of quad-pol SAR data from compact polarimetry data for ocean target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1): 591–600. doi: 10.1109/TGRS.2012.2199760
    [23]
    LI Haiyan, WU Jin, PERRIE W, et al. Wind speed retrieval from hybrid-pol compact polarization synthetic aperture radar images[J]. IEEE Journal of Oceanic Engineering, 2018, 43(3): 713–724. doi: 10.1109/JOE.2017.2722225
    [24]
    LI Yu, ZHANG Yuanzhi, CHEN Jie, et al. Improved compact polarimetric SAR quad-pol reconstruction algorithm for oil spill detection[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(6): 1139–1142. doi: 10.1109/lgrs.2013.2288336
    [25]
    ESPESETH M M, BREKKE C, and ANFINSEN S N. Hybrid-polarity and reconstruction methods for sea ice with L-and C-band SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 467–471. doi: 10.1109/LGRS.2016.2519824
    [26]
    KUMAR A and PANIGRAHI R K. Entropy based reconstruction technique for analysis of hybrid-polarimetric SAR data[J]. IET Radar, Sonar & Navigation, 2019, 13(4): 620–626. doi: 10.1049/iet-rsn.2018.5338
    [27]
    YUE Dongxiao, XU Feng, and JIN Yaqiu. Wishart-Bayesian reconstruction of Quad-Pol from Compact-Pol SAR image[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(9): 1623–1627. doi: 10.1109/LGRS.2017.2727280
    [28]
    REIGBER A, NEUMANN M, FERRO-FAMIL L, et al. Multi-baseline coherence optimisation in partial and compact polarimetric modes[C]. 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, USA, 2008: 597–600. doi: 10.1109/IGARSS.2008.4779063.
    [29]
    RANEY R K. Comparing compact and quadrature polarimetric SAR performance[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(6): 861–864. doi: 10.1109/lgrs.2016.2550863
    [30]
    RANEY R K. Hybrid dual-polarization synthetic aperture radar[J]. Remote Sensing, 2019, 11(13): 1521. doi: 10.3390/rs11131521
    [31]
    RANEY R K. Dual-polarized SAR and stokes parameters[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(3): 317–319. doi: 10.1109/LGRS.2006.871746
    [32]
    CHARBONNEAU F J, BRISCO B, RANEY R K, et al. Compact polarimetry: Multi-thematic evaluation[C]. The 4th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry (PolInSAR), Frascati, Italy, 2009, 26–30.
    [33]
    RANEY R K, CAHILL J T S, PATTERSON G W, et al. The m-chi decomposition of hybrid dual-polarimetric radar data[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich: Germany, 2012, 5093–5096. doi: 10.1109/IGARSS.2012.6352465.
    [34]
    CLOUDE S R, GOODENOUGH D G, and CHEN H. Compact decomposition theory[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(1): 28–32. doi: 10.1109/LGRS.2011.2158983
    [35]
    SABRY R and VACHON P W. A unified framework for general compact and quad polarimetric SAR data and imagery analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 582–602. doi: 10.1109/TGRS.2013.2242479
    [36]
    GUO R, LIU Y B, WU Y H, et al. Applying H/α decomposition to compact polarimetric SAR[J]. IET Radar, Sonar & Navigation, 2012, 6(2): 61–70. doi: 10.1049/iet-rsn.2011.0007
    [37]
    ZHANG Hong, XIE Lei, WANG Chao, et al. Investigation of the capability of H-α decomposition of compact polarimetric SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(4): 868–872. doi: 10.1109/LGRS.2013.2280456
    [38]
    谢镭. 多模式极化SAR图像分解与分类方法及应用研究[D]. [博士论文], 中国科学院大学, 2016: 46–59.

    XIE Lei. Researches on methods and applications of image decomposition and classification for multi-mode polarimetric SAR[D]. [Ph.D. dissertation], University of Chinese Academy of Sciences, 2016: 46–59.
    [39]
    GUO Rui, HE Wei, ZHANG Shuangxi, et al. Analysis of three-component decomposition to compact polarimetric synthetic aperture radar[J]. IET Radar, Sonar & Navigation, 2014, 8(6): 685–691. doi: 10.1049/iet-rsn.2013.0114
    [40]
    刘萌, 张红, 王超. 基于简缩极化数据的三分量分解模型[J]. 电波科学学报, 2012, 27(2): 365–371.

    LIU Meng, ZHANG Hong, and WANG Chao. Three-component scattering model for compact polarimetric SAR data[J]. Chinese Journal of Radio Science, 2012, 27(2): 365–371.
    [41]
    HAN Kuoye, JIANG Mian, WANG Mingjiang, et al. Compact polarimetric SAR interferometry target decomposition with the freeman-durden method[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(8): 2847–2861. doi: 10.1109/JSTARS.2018.2842125
    [42]
    KUMAR A, DAS A, and PANIGRAHI R K. Hybrid-pol based three-component scattering model for analysis of RISAT data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(12): 5155–5162. doi: 10.1109/JSTARS.2017.2768378
    [43]
    AINSWORTH T L, KELLY J P, and LEE J S. Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(5): 464–471. doi: 10.1016/j.isprsjprs.2008.12.008
    [44]
    KUMAR V, RAO Y S, BHATTACHARYA A, et al. Classification assessment of real versus simulated compact and quad-pol modes of ALOS-2[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(9): 1497–1501. doi: 10.1109/LGRS.2019.2899268
    [45]
    CHARBONNEAU F J, BRISCO B, RANEY R K, et al. Compact polarimetry overview and applications assessment[J]. Canadian Journal of Remote Sensing, 2010, 36(S2): S298–S315. doi: 10.5589/m10-062
    [46]
    OHKI M and SHIMADA M. Large-area land use and land cover classification with quad, compact, and dual polarization SAR data by PALSAR-2[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9): 5550–5557. doi: 10.1109/TGRS.2018.2819694
    [47]
    BRISCO B, LI K, TEDFORD B, et al. Compact polarimetry assessment for rice and wetland mapping[J]. International Journal of Remote Sensing, 2013, 34(6): 1949–1964. doi: 10.1080/01431161.2012.730156
    [48]
    XU Lu, ZHANG Hong, and WANG Chao. Comparative analysis of classification results between compact and fully polarimetric SAR images in random forest classifier[C]. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Fort Worth, USA, 2017: 3929–3932. doi: 10.1109/IGARSS.2017.8127859.
    [49]
    XU Lu, ZHANG Hong, WANG Chao, et al. Corn mapping uisng multi-temporal fully and compact SAR data[C]. 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China, 2017. doi: 10.1109/BIGSARDATA.2017.8124925.
    [50]
    MAHDIANPARI M, MOHAMMADIMANESH F, MCNAIRN H, et al. Mid-season crop classification using dual-, compact-, and full-polarization in preparation for the Radarsat Constellation Mission (RCM)[J]. Remote Sensing, 2019, 11(13): 1582. doi: 10.3390/rs11131582
    [51]
    XIE Lei, ZHANG Hong, WU Fan, et al. Capability of rice mapping using hybrid polarimetric SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 3812–3822. doi: 10.1109/JSTARS.2014.2387214
    [52]
    XIE Lei, ZHANG Hong, LI Hongzhong, et al. A unified framework for crop classification in southern China using fully polarimetric, dual polarimetric, and compact polarimetric SAR data[J]. International Journal of Remote Sensing, 2015, 36(14): 3798–3818. doi: 10.1080/01431161.2015.1070319
    [53]
    UPPALA D, KOTHAPALLI R V, POLOJU S, et al. Rice crop discrimination using single date RISAT1 hybrid (RH, RV) polarimetric data[J]. Photogrammetric Engineering & Remote Sensing, 2015, 81(7): 557–563. doi: 10.14358/PERS.81.7.557
    [54]
    UPPALA D, VENKATA R K, POLOJU S, et al. Discrimination of maize crop with hybrid polarimetric RISAT1 data[J]. International Journal of Remote Sensing, 2016, 37(11): 2641–2652. doi: 10.1080/01431161.2016.1184353
    [55]
    国贤玉, 李坤, 王志勇, 等. 基于SVM+SFS策略的多时相紧致极化SAR水稻精细分类[J]. 国土资源遥感, 2018, 30(4): 20–27. doi: 10.6046/gtzyyg.2018.04.04

    GUO Xianyu, LI Kun, WANG Zhiyong, et al. Fine classification of rice with multi-temporal compact polarimetric SAR based on SVM +SFS strategy[J]. Remote Sensing for Land &Resources, 2018, 30(4): 20–27. doi: 10.6046/gtzyyg.2018.04.04
    [56]
    CHIRAKKAL S, HALDAR D, and MISRA A. Evaluation of hybrid polarimetric decomposition techniques for winter crop discrimination[J]. Progress in Electromagnetics Research M, 2017, 55: 73–84. doi: 10.2528/PIERM17011603
    [57]
    BALLESTER-BERMAN J D, and LOPEZ-SANCHEZ J M. Time series of hybrid-polarity parameters over agricultural crops[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(1): 139–143. doi: 10.1109/LGRS.2011.2162312
    [58]
    ZHANG Wangfei, LI Zengyuan, CHEN Erxue, et al. Compact polarimetric response of rape (Brassica napus L.) at C-band: Analysis and growth parameters inversion[J]. Remote Sensing, 2017, 9(6): 591. doi: 10.3390/rs9060591
    [59]
    DAVE V A, HALDAR D, DAVE R, et al. Cotton crop biophysical parameter study using hybrid/compact polarimetric RISAT-1 SAR data[J]. Progress in Electromagnetics Research M, 2017, 57: 185–196. doi: 10.2528/PIERM16121903
    [60]
    HOMAYOUNI S, MCNAIRN H, HOSSEINI M, et al. Quad and compact multitemporal C-band PolSAR observations for crop characterization and monitoring[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 74: 78–87. doi: 10.1016/j.jag.2018.09.009
    [61]
    GUO Xianyu, LI Kun, SHAO Yun, et al. Inversion of rice biophysical parameters using simulated compact polarimetric SAR C-band data[J]. Sensors, 2018, 18(7): 2271. doi: 10.3390/s18072271
    [62]
    LIU Changan, CHEN Zhongxin, HAO Pengyu, et al. LAI Retrieval of winter wheat using simulated compact SAR data through GA-PLS modeling[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 3840–3843. doi: 10.1109/IGARSS.2018.8518005.
    [63]
    YANG Zhi, LI Kun, LIU Long, et al. Rice growth monitoring using simulated compact polarimetric C band SAR[J]. Radio Science, 2014, 49(12): 1300–1315. doi: 10.1002/2014RS005498
    [64]
    YANG Zhi, SHAO Yun, LI Kun, et al. An improved scheme for rice phenology estimation based on time-series multispectral HJ-1A/B and polarimetric RADARSAT-2 data[J]. Remote Sensing of Environment, 2017, 195: 184–201. doi: 10.1016/j.rse.2017.04.016
    [65]
    LOPEZ-SANCHEZ J M, VICENTE-GUIJALBA F, BALLESTER-BERMAN J D, et al. Polarimetric response of rice fields at C-band: Analysis and phenology retrieval[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 2977–2993. doi: 10.1109/TGRS.2013.2268319
    [66]
    IZUMI Y, DEMIRCI S, BIN BAHARUDDIN M, et al. Analysis of dual-and full-circular polarimetric SAR modes for rice phenology monitoring: An experimental investigation through ground-based measurements[J]. Applied Sciences, 2017, 7(4): 368. doi: 10.3390/app7040368
    [67]
    ATTEIA G and COLLINS M J. Ship detection performance assessment for simulated RCM SAR data[C]. 2014 IEEE Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, Canada, 2014: 553–556. doi: 10.1109/IGARSS.2014.6946482.
    [68]
    SHIRVANY R, CHABERT M, and TOURNERET J Y. Ship and oil-spill detection using the degree of polarization in linear and hybrid/compact dual-pol SAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(3): 885–892. doi: 10.1109/JSTARS.2012.2182760
    [69]
    YIN Junjun and YANG Jian. Ship detection by using the M-Chi and M-Delta decompositions[C]. 2014 IEEE Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, Canada, 2014: 2738–2741. doi: 10.1109/IGARSS.2014.6947042.
    [70]
    曹成会, 张杰, 张晰, 等. C波段紧缩极化合成孔径雷达船只目标检测性能分析[J]. 中国海洋大学学报, 2017, 47(2): 85–93. doi: 10.16441/j.cnki.hdxb.20160347

    CAO Chenghui, ZHANG Jie, ZHANG Xi, et al. The analysis of ship target detection performance with C band compact polarimetric SAR[J]. Periodical of Ocean University of China, 2017, 47(2): 85–93. doi: 10.16441/j.cnki.hdxb.20160347
    [71]
    XU Lu, ZHANG Hong, WANG Chao, et al. Compact polarimetric SAR ship detection with m-δ decomposition using visual attention model[J]. Remote Sensing, 2016, 8(9): 751. doi: 10.3390/rs8090751
    [72]
    FAN Qiancong, CHEN Feng, CHENG Ming, et al. A modified framework for ship detection from compact polarization SAR image[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 3539–3542. doi: 10.1109/IGARSS.2018.8518763.
    [73]
    FAN Qiancong, CHEN Feng, CHENG Ming, et al. Ship detection using a fully convolutional network with compact polarimetric sar images[J]. Remote Sensing, 2019, 11(18): 2171. doi: 10.3390/rs11182171
    [74]
    GAO Gui, GAO Sheng, HE Juan, et al. Adaptive ship detection in hybrid-polarimetric SAR images based on the power-entropy decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9): 5394–5407. doi: 10.1109/TGRS.2018.2815592
    [75]
    GAO Gui, GAO Sheng, HE Juan, et al. Ship detection using compact polarimetric SAR based on the notch filter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9): 5380–5393. doi: 10.1109/TGRS.2018.2815582
    [76]
    JI Kefeng, LENG Xiangguang, WANG Haibo, et al. Ship detection using weighted SVM and M-CHI decomposition in compact polarimetric SAR imagery[C]. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, USA, 2017: 890–893. doi: 10.1109/IGARSS.2017.8127095.
    [77]
    CAO Chenghui, ZHANG Jie, MENG Junmei, et al. Analysis of ship detection performance with full-, compact-and dual-polarimetric SAR[J]. Remote Sensing, 2019, 11(18): 2160. doi: 10.3390/rs11182160
    [78]
    ZHANG Biao, LI Xiaofeng, PERRIE W, et al. Compact polarimetric synthetic aperture radar for marine oil platform and slick detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1407–1423. doi: 10.1109/TGRS.2016.2623809
    [79]
    LI Haiyan, PERRIE W, HE Yijun, et al. Target detection on the ocean with the relative phase of compact polarimetry SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3299–3305. doi: 10.1109/TGRS.2012.2224119
    [80]
    LI Haiyan, PERRIE W, HE Yijun, et al. Analysis of the polarimetric SAR scattering properties of oil-covered waters[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 3751–3759. doi: 10.1109/JSTARS.2014.2348173
    [81]
    KUMAR L J V, KISHORE K J, and RAO K P. Decomposition methods for detection of oil spills based on RISAT-1 SAR images[J]. International Journal of Remote Sensing & Geoscience, 2014, 3(4): 2319–3484.
    [82]
    MIGLIACCIO M, NUNZIATA F, and BUONO A. SAR polarimetry for sea oil slick observation[J]. International Journal of Remote Sensing, 2015, 36(12): 3243–3273. doi: 10.1080/01431161.2015.1057301
    [83]
    YIN Junjun, YANG Jian, ZHOU Zhengshu, et al. The extended Bragg scattering model-based method for ship and oil-spill observation using compact polarimetric SAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 3760–3772. doi: 10.1109/JSTARS.2014.2359141
    [84]
    NUNZIATA F, MIGLIACCIO M, and LI Xiaofeng. Sea oil slick observation using hybrid-polarity SAR architecture[J]. IEEE Journal of Oceanic Engineering, 2015, 40(2): 426–440. doi: 10.1109/JOE.2014.2329424
    [85]
    BUONO A, NUNZIATA F, MIGLIACCIO M, et al. Polarimetric analysis of compact-polarimetry SAR architectures for sea oil slick observation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 5862–5874. doi: 10.1109/TGRS.2016.2574561
    [86]
    ZHANG Yuanzhi, LI Yu, LIANG X S, et al. Comparison of oil spill classifications using fully and compact polarimetric SAR images[J]. Applied Sciences, 2017, 7(2): 193. doi: 10.3390/app7020193
    [87]
    谢广奇, 杨帅, 陈启浩, 等. 简缩极化特征值分析的溢油检测[J]. 遥感学报, 2019, 23(2): 303–312. doi: 10.11834/jrs.20197260

    XIE Guangqi, YANG Shuai, CHEN Qihao, et al. Oil spill detection based on compact polarimetric eigenvalue decomposition[J]. Journal of Remote Sensing, 2019, 23(2): 303–312. doi: 10.11834/jrs.20197260
    [88]
    DABBOOR M, SINGHA S, TOPOUZELIS K, et al. Oil spill detection using simulated radarsat constellation mission compact polarimetric SAR data[C]. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, USA, 2017: 4582–4585. doi: 10.1109/IGARSS.2017.8128021.
    [89]
    DABBOOR M, SINGHA S, MONTPETIT B, et al. Assessment of simulated compact polarimetry of the RCM medium resolution SAR modes for oil spill detection[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 2416–2419. doi: 10.1109/IGARSS.2018.8517756.
    [90]
    DABBOOR M, SINGHA S, MONTPETIT B, et al. Pre-launch assessment of RADARSAT constellation mission medium resolution modes for sea oil slicks and lookalike discrimination[J]. Canadian Journal of Remote Sensing, 2019, 45(3/4): 530–549. doi: 10.1080/07038992.2019.1659722
    [91]
    LI Haiyan and PERRIE W. Sea ice characterization and classification using hybrid polarimetry SAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(11): 4998–5010. doi: 10.1109/JSTARS.2016.2584542
    [92]
    SINGHA S and RESSEL R. Arctic sea ice characterization using RISAT-1 compact-pol SAR imagery and feature evaluation: A case study over Northeast Greenland[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3504–3514. doi: 10.1109/JSTARS.2017.2691258
    [93]
    SINGHA S. Potential of compact polarimetry for operational sea ice monitoring over arctic and Antarctic region[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 7113–7116. doi: 10.1109/IGARSS.2018.8517653.
    [94]
    ESPESETH M M, BREKKE C, and JOHANSSON A M. Assessment of RISAT-1 and radarsat-2 for sea ice observations from a hybrid-polarity perspective[J]. Remote Sensing, 2017, 9(11): 1088. doi: 10.3390/rs9111088
    [95]
    NASONOVA S, SCHARIEN R K, GELDSETZER T, et al. Optimal compact polarimetric parameters and texture features for discriminating sea ice types during winter and advanced melt[J]. Canadian Journal of Remote Sensing, 2018, 44(4): 390–411. doi: 10.1080/07038992.2018.1527683
    [96]
    DABBOOR M, MONTPETIT B, and HOWELL S. Assessment of simulated compact polarimetry of the high resolution radarsat constellation mission SAR mode for multiyear and first year sea ice characterization[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 2420–2423. doi: 10.1109/IGARSS.2018.8517737.
    [97]
    GHANBARI M, CLAUSI D A, XU Linlin, et al. Contextual classification of sea-ice types using compact polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(10): 7476–7491. doi: 10.1109/TGRS.2019.2913796
    [98]
    TRUONG-LOI M L, FREEMAN A, DUBOIS-FERNANDEZ P C, et al. Estimation of soil moisture and Faraday rotation from bare surfaces using compact polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(11): 3608–3615. doi: 10.1109/TGRS.2009.2031428
    [99]
    PONNURANGAM G G, JAGDHUBER T, HAJNSEK I, et al. Soil moisture estimation using hybrid polarimetric SAR data of RISAT-1[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4): 2033–2049. doi: 10.1109/TGRS.2015.2494860
    [100]
    SANTI E, PETTINATO S, PALOSCIA S, et al. Estimating soil moisture from C and X band Sar using machine learning algorithms and compact polarimetry[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 1426–1429. doi: 10.1109/IGARSS.2018.8518469.
    [101]
    PONNURANGAM G G and RAO Y S. The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas[J]. International Journal of Remote Sensing, 2018, 39(22): 8337–8360. doi: 10.1080/01431161.2018.1488281
    [102]
    LAVALLE M, SOLIMINI D, POTTIER E, et al. Compact polarimetric SAR interferometry[J]. IET Radar, Sonar & Navigation, 2010, 4(3): 449–456. doi: 10.1049/iet-rsn.2009.0049
    [103]
    DUBOIS-FERNANDEZ P C, SOUYRIS J C, ANGELLIAUME S, et al. The compact polarimetry alternative for spaceborne SAR at low frequency[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(10): 3208–3222. doi: 10.1109/TGRS.2008.919143
    [104]
    谈璐璐, 杨立波, 杨汝良. 合成孔径雷达简缩极化干涉数据的植被高度反演技术研究[J]. 电子与信息学报, 2010, 32(12): 2814–2819. doi: 10.3724/SP.J.1146.2010.00091

    TAN Lulu, YANG Libo, and YANG Ruliang. Investigation on vegetation height retrieval technique with compact PolInSAR data[J]. Journal of Electronics &Information Technology, 2010, 32(12): 2814–2819. doi: 10.3724/SP.J.1146.2010.00091
    [105]
    RAMACHANDRAN N and DIKSHIT O. Experimental validation of compact tomosar for vegetation characterization[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018: 6727–6730. doi: 10.1109/IGARSS.2018.8517824.
    [106]
    SABRY R and AINSWORTH T L. SAR compact polarimetry for change detection and characterization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(3): 898–909. doi: 10.1109/JSTARS.2019.2896536
    [107]
    ZHANG Xuefei, ZHANG Hong, and WANG Chao. Water-change detection with Chinese Gaofen-3 simulated compact polarimetric SAR images[C]. 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China, 2017. doi: 10.1109/BIGSARDATA.2017.8124940.
    [108]
    MAHDIANPARI M, SALEHI B, MOHAMMADIMANESH F, et al. An assessment of simulated compact polarimetric SAR data for wetland classification using random forest algorithm[J]. Canadian Journal of Remote Sensing, 2017, 43(5): 468–484. doi: 10.1080/07038992.2017.1381550
    [109]
    DABBOOR M, BRISCO B, BANKS S, et al. Multitemporal monitoring of wetlands using simulated radarsat constellation mission compact polarimetric SAR data[C]. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, USA, 2017: 4586–4589. doi: 10.1109/IGARSS.2017.8128022.
    [110]
    DABBOOR M, BANKS S, WHITE L, et al. Comparison of compact and fully polarimetric SAR for multitemporal wetland monitoring[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(5): 1417–1430. doi: 10.1109/JSTARS.2019.2909437
    [111]
    MOHAMMADIMANESH F, SALEHI B, MAHDIANPARI M, et al. Full and simulated compact polarimetry sar responses to Canadian wetlands: Separability analysis and classification[J]. Remote Sensing, 2019, 11(5): 516. doi: 10.3390/rs11050516
    [112]
    BANKS S, MILLARD K, BEHNAMIAN A, et al. Contributions of actual and simulated satellite SAR data for substrate type differentiation and shoreline mapping in the Canadian arctic[J]. Remote Sensing, 2017, 9(12): 1206. doi: 10.3390/rs9121206
    [113]
    WHITE L, MILLARD K, BANKS S, et al. Moving to the RADARSAT constellation mission: Comparing synthesized compact polarimetry and dual polarimetry data with fully polarimetric RADARSAT-2 data for image classification of peatlands[J]. Remote Sensing, 2017, 9(6): 573. doi: 10.3390/rs9060573
    [114]
    FOBERT M A, SPRAY J G, and SINGHROY V. Assessing the benefits of simulated RADARSAT constellation mission polarimetry images for structural mapping of an impact crater in the Canadian shield[J]. Canadian Journal of Remote Sensing, 2018, 44(4): 321–336. doi: 10.1080/07038992.2018.1517022
    [115]
    BRISCO B, SHELAT Y, MURNAGHAN K, et al. Evaluation of C-band SAR for identification of flooded vegetation in emergency response products[J]. Canadian Journal of Remote Sensing, 2019, 45(1): 73–87. doi: 10.1080/07038992.2019.1612236
    [116]
    LIU Yin, LI Linlin, CHEN Qihao, et al. Building damage assessment of compact polarimetric SAR using statistical model texture parameter[C]. 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China, 2017. doi: 10.1109/BIGSARDATA.2017.8124923.
    [117]
    JEON W and KIM Y. Investigation of hybrid polarimetric features for tsunami-induced damage assessment of urban areas[J]. Remote Sensing Letters, 2019, 10(10): 988–997. doi: 10.1080/2150704x.2019.1637957
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%Taichung: 0.0 %Taichung: 0.0 %Taiwan, China: 0.0 %Taiwan, China: 0.0 %Thane: 0.0 %Thane: 0.0 %United Kingdom: 0.0 %United Kingdom: 0.0 %United States: 0.2 %United States: 0.2 %Viet Nam: 0.0 %Viet Nam: 0.0 %Wyoming: 0.0 %Wyoming: 0.0 %[]: 1.8 %[]: 1.8 %三亚: 0.1 %三亚: 0.1 %三明: 0.0 %三明: 0.0 %上海: 2.3 %上海: 2.3 %上饶: 0.1 %上饶: 0.1 %东京: 0.1 %东京: 0.1 %东京都: 0.0 %东京都: 0.0 %东莞: 0.4 %东莞: 0.4 %中卫: 0.0 %中卫: 0.0 %中山: 0.0 %中山: 0.0 %丹东: 0.0 %丹东: 0.0 %乌海: 0.0 %乌海: 0.0 %乐山: 0.0 %乐山: 0.0 %九江: 0.1 %九江: 0.1 %亳州: 0.0 %亳州: 0.0 %伊斯兰堡: 0.0 %伊斯兰堡: 0.0 %休斯敦: 0.1 %休斯敦: 0.1 %伦敦: 0.0 %伦敦: 0.0 %佛山: 0.1 %佛山: 0.1 %佳木斯: 0.0 %佳木斯: 0.0 %保定: 0.1 %保定: 0.1 %信阳: 0.0 %信阳: 0.0 %六安: 0.1 %六安: 0.1 %兰州: 0.2 %兰州: 0.2 %内江: 0.0 %内江: 0.0 %凤凰城: 0.0 %凤凰城: 0.0 %加利福尼亚州: 0.0 %加利福尼亚州: 0.0 %包头: 0.0 %包头: 0.0 %北京: 11.2 %北京: 11.2 %北海: 0.0 %北海: 0.0 %十堰: 0.1 %十堰: 0.1 %南京: 4.8 %南京: 4.8 %南充: 0.0 %南充: 0.0 %南宁: 0.2 %南宁: 0.2 %南平: 0.0 %南平: 0.0 %南昌: 0.3 %南昌: 0.3 %南荷兰省: 0.0 %南荷兰省: 0.0 %南通: 0.1 %南通: 0.1 %卡纳塔克邦: 0.0 %卡纳塔克邦: 0.0 %印度尼西亚北苏门答腊: 0.0 %印度尼西亚北苏门答腊: 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%库比蒂诺: 0.0 %库比蒂诺: 0.0 %廊坊: 0.0 %廊坊: 0.0 %延安: 0.0 %延安: 0.0 %开封: 0.0 %开封: 0.0 %张家口: 0.8 %张家口: 0.8 %徐州: 0.1 %徐州: 0.1 %德罕: 0.0 %德罕: 0.0 %德里: 0.0 %德里: 0.0 %德阳: 0.0 %德阳: 0.0 %德黑兰: 0.0 %德黑兰: 0.0 %怀化: 0.0 %怀化: 0.0 %悉尼: 0.1 %悉尼: 0.1 %惠州: 0.0 %惠州: 0.0 %意法半: 0.0 %意法半: 0.0 %成都: 3.7 %成都: 3.7 %扬州: 0.3 %扬州: 0.3 %抚州: 0.0 %抚州: 0.0 %拉斯维加斯: 0.0 %拉斯维加斯: 0.0 %拉萨: 0.0 %拉萨: 0.0 %拉贾斯坦邦: 0.0 %拉贾斯坦邦: 0.0 %拉雷多: 0.0 %拉雷多: 0.0 %揭阳: 0.0 %揭阳: 0.0 %新伦敦: 0.0 %新伦敦: 0.0 %新加坡: 0.1 %新加坡: 0.1 %新北: 0.0 %新北: 0.0 %新竹: 0.0 %新竹: 0.0 %无锡: 0.3 %无锡: 0.3 %日照: 0.0 %日照: 0.0 %昆明: 0.8 %昆明: 0.8 %晋中: 0.0 %晋中: 0.0 %晋城: 0.0 %晋城: 0.0 %朝阳: 0.0 %朝阳: 0.0 %杭州: 1.7 %杭州: 1.7 %枣庄: 0.0 %枣庄: 0.0 %柳州: 0.0 %柳州: 0.0 %株洲: 0.0 %株洲: 0.0 %格兰特县: 0.0 %格兰特县: 0.0 %格拉沃利讷: 0.1 %格拉沃利讷: 0.1 %桂林: 0.1 %桂林: 0.1 %梅州: 0.0 %梅州: 0.0 %榆林: 0.1 %榆林: 0.1 %橙: 0.0 %橙: 0.0 %武汉: 1.0 %武汉: 1.0 %毕节: 0.0 %毕节: 0.0 %永州: 0.0 %永州: 0.0 %汉中: 0.0 %汉中: 0.0 %汕头: 0.1 %汕头: 0.1 %江门: 0.0 %江门: 0.0 %池州: 0.0 %池州: 0.0 %沈阳: 0.2 %沈阳: 0.2 %沧州: 0.0 %沧州: 0.0 %泰州: 0.2 %泰州: 0.2 %泰米尔纳德: 0.0 %泰米尔纳德: 0.0 %泸州: 0.0 %泸州: 0.0 %洛阳: 0.3 %洛阳: 0.3 %济南: 0.3 %济南: 0.3 %济宁: 0.0 %济宁: 0.0 %济源: 0.1 %济源: 0.1 %海口: 0.0 %海口: 0.0 %海西: 0.0 %海西: 0.0 %淮北: 0.0 %淮北: 0.0 %淮南: 0.1 %淮南: 0.1 %深圳: 1.5 %深圳: 1.5 %清州: 0.0 %清州: 0.0 %清远: 0.0 %清远: 0.0 %渥太华: 0.1 %渥太华: 0.1 %温州: 0.1 %温州: 0.1 %渭南: 0.1 %渭南: 0.1 %湖州: 0.0 %湖州: 0.0 %湘潭: 0.0 %湘潭: 0.0 %湘西: 0.0 %湘西: 0.0 %湛江: 0.0 %湛江: 0.0 %滁州: 0.0 %滁州: 0.0 %漯河: 0.6 %漯河: 0.6 %潍坊: 0.0 %潍坊: 0.0 %潮州: 0.0 %潮州: 0.0 %澳门特别行政区: 0.0 %澳门特别行政区: 0.0 %濮阳: 0.0 %濮阳: 0.0 %烟台: 0.1 %烟台: 0.1 %焦作: 0.1 %焦作: 0.1 %牡丹江: 0.0 %牡丹江: 0.0 %特伦甘地: 0.1 %特伦甘地: 0.1 %珠海: 0.1 %珠海: 0.1 %白山: 0.0 %白山: 0.0 %白银: 0.0 %白银: 0.0 %眉山: 0.0 %眉山: 0.0 %石家庄: 0.6 %石家庄: 0.6 %福州: 0.3 %福州: 0.3 %秦皇岛: 0.3 %秦皇岛: 0.3 %纽敦: 0.0 %纽敦: 0.0 %纽约: 0.1 %纽约: 0.1 %绍兴: 0.1 %绍兴: 0.1 %绥化: 0.0 %绥化: 0.0 %绵阳: 0.2 %绵阳: 0.2 %罗马: 0.0 %罗马: 0.0 %美国加利福尼亚圣地亚哥: 0.0 %美国加利福尼亚圣地亚哥: 0.0 %肇庆: 0.0 %肇庆: 0.0 %胡志明: 0.1 %胡志明: 0.1 %自贡: 0.0 %自贡: 0.0 %舟山: 0.0 %舟山: 0.0 %芒廷维尤: 17.8 %芒廷维尤: 17.8 %芜湖: 0.0 %芜湖: 0.0 %芝加哥: 0.4 %芝加哥: 0.4 %苏州: 0.6 %苏州: 0.6 %莫斯科: 0.0 %莫斯科: 0.0 %莱芜: 0.0 %莱芜: 0.0 %菏泽: 0.0 %菏泽: 0.0 %萨尔州: 0.0 %萨尔州: 0.0 %葫芦岛: 0.0 %葫芦岛: 0.0 %蚌埠: 0.0 %蚌埠: 0.0 %衡水: 0.2 %衡水: 0.2 %衡阳: 0.1 %衡阳: 0.1 %衢州: 0.1 %衢州: 0.1 %西宁: 11.4 %西宁: 11.4 %西安: 2.8 %西安: 2.8 %西班牙: 0.0 %西班牙: 0.0 %西雅图: 0.0 %西雅图: 0.0 %诺伊达: 0.0 %诺伊达: 0.0 %诺沃克: 0.0 %诺沃克: 0.0 %贵阳: 0.3 %贵阳: 0.3 %赣州: 0.0 %赣州: 0.0 %赤峰: 0.0 %赤峰: 0.0 %车士活: 0.0 %车士活: 0.0 %达州: 0.1 %达州: 0.1 %运城: 0.3 %运城: 0.3 %遂宁: 0.0 %遂宁: 0.0 %邢台: 0.1 %邢台: 0.1 %邯郸: 0.1 %邯郸: 0.1 %郑州: 0.7 %郑州: 0.7 %郴州: 0.0 %郴州: 0.0 %鄂州: 0.0 %鄂州: 0.0 %重庆: 0.8 %重庆: 0.8 %金华: 0.1 %金华: 0.1 %金奈: 0.1 %金奈: 0.1 %铁岭: 0.0 %铁岭: 0.0 %银川: 0.0 %银川: 0.0 %锦州: 0.0 %锦州: 0.0 %镇江: 0.2 %镇江: 0.2 %长春: 0.3 %长春: 0.3 %长沙: 1.9 %长沙: 1.9 %长治: 0.0 %长治: 0.0 %阜阳: 0.0 %阜阳: 0.0 %阳泉: 0.0 %阳泉: 0.0 %阿坝: 0.0 %阿坝: 0.0 %阿姆斯特丹: 0.0 %阿姆斯特丹: 0.0 %阿尔泽特河畔埃施: 0.0 %阿尔泽特河畔埃施: 0.0 %阿布扎比: 0.1 %阿布扎比: 0.1 %阿德莱德: 0.1 %阿德莱德: 0.1 %霍德夏沙隆: 0.0 %霍德夏沙隆: 0.0 %青岛: 0.6 %青岛: 0.6 %鞍山: 0.0 %鞍山: 0.0 %韶关: 0.0 %韶关: 0.0 %香港: 0.1 %香港: 0.1 %香港特别行政区: 0.2 %香港特别行政区: 0.2 %马鞍山: 0.0 %马鞍山: 0.0 %黄冈: 0.0 %黄冈: 0.0 %齐齐哈尔: 0.1 %齐齐哈尔: 0.1 %龟尾: 0.0 %龟尾: 0.0 %其他其他ArlingtonAustraliaBadenBangladeshBelgiumCanadaCantonCentral DistrictChinaCzech RepublicEuropean UnionFalls ChurchGermanyGreeceHanoiHerndonIndiaIndonesiaKorea Republic ofMacauMatawanMoroccoNorth PointSaudi ArabiaSeattleSecaucusState CollegeSwedenTaichungTaiwan, ChinaThaneUnited KingdomUnited StatesViet NamWyoming[]三亚三明上海上饶东京东京都东莞中卫中山丹东乌海乐山九江亳州伊斯兰堡休斯敦伦敦佛山佳木斯保定信阳六安兰州内江凤凰城加利福尼亚州包头北京北海十堰南京南充南宁南平南昌南荷兰省南通卡纳塔克邦印度尼西亚北苏门答腊厦门台中台北台州台湾省合肥吉林吉隆坡呼和浩特咸阳哈尔滨哥伦布唐山商丘嘉兴圣地亚哥圣安东尼奥圣彼得堡圣约翰斯埃德蒙顿墨尔本大庆大连天水围天津太原奥卢奥尔巴尼威海娄底孟买宁波安卡拉安康安顺宜兰宜宾宜春宝鸡宣城密蘇里城巴中巴伐利亚州巴音郭楞巴音郭楞蒙古自治州布兰普顿常州常德平顶山广州庆阳库比蒂诺廊坊延安开封张家口徐州德罕德里德阳德黑兰怀化悉尼惠州意法半成都扬州抚州拉斯维加斯拉萨拉贾斯坦邦拉雷多揭阳新伦敦新加坡新北新竹无锡日照昆明晋中晋城朝阳杭州枣庄柳州株洲格兰特县格拉沃利讷桂林梅州榆林橙武汉毕节永州汉中汕头江门池州沈阳沧州泰州泰米尔纳德泸州洛阳济南济宁济源海口海西淮北淮南深圳清州清远渥太华温州渭南湖州湘潭湘西湛江滁州漯河潍坊潮州澳门特别行政区濮阳烟台焦作牡丹江特伦甘地珠海白山白银眉山石家庄福州秦皇岛纽敦纽约绍兴绥化绵阳罗马美国加利福尼亚圣地亚哥肇庆胡志明自贡舟山芒廷维尤芜湖芝加哥苏州莫斯科莱芜菏泽萨尔州葫芦岛蚌埠衡水衡阳衢州西宁西安西班牙西雅图诺伊达诺沃克贵阳赣州赤峰车士活达州运城遂宁邢台邯郸郑州郴州鄂州重庆金华金奈铁岭银川锦州镇江长春长沙长治阜阳阳泉阿坝阿姆斯特丹阿尔泽特河畔埃施阿布扎比阿德莱德霍德夏沙隆青岛鞍山韶关香港香港特别行政区马鞍山黄冈齐齐哈尔龟尾1/2

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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