Volume 9 Issue 3
Jun.  2020
Turn off MathJax
Article Contents
Abstract
References
Article Navigation >  Journal of Radars  > 2020  >  9(3): 578-587
Zhang Yue, Zou Huanxin, Shao Ningyuan, et al.. Fast superpixel segmentation algorithm for PolSAR images[J]. Journal of Radars, 2017, 6(5): 564–573. DOI: 10.12000/JR17018
Citation: PANG Lei, ZHANG Fengli, WANG Guojun, et al. Imaging simulation and damage assessment feature analysis of Ku band polarized SAR of buildings[J]. Journal of Radars, 2020, 9(3): 578–587. doi: 10.12000/JR20061
shu

Imaging Simulation and Damage Assessment Feature Analysis of Ku Band Polarized SAR of Buildings

DOI: 10.12000/JR20061 CSTR: 32380.14.JR20061
  • 1.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

  • 3.

    China Science and Technology Satellite Application Deqing Research Institute, Zhejiang key laboratory of microwave target characteristic measurement and remote sensing, Huzhou 313200, China

  • 4.

    Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Funds:  The National Key R&D Program of China (2016YFB0502504), The National Natural Science Foundation of China (41671359), The “Practical Training Plan” Project for Cross Training of High Level Talents in Beijing Colleges and Universities (No. 17)
More Information
  • Corresponding author: ZHANG Fengli, zhangfl@aircas.ac.cn
  • Received Date: 2020-05-13
  • Rev Recd Date: 2020-06-24
    • Available Online: 2020-07-01
  • Publish Date: 2020-06-01
    Abstract
  • Building damage assessment is important in disaster emergency monitoring. In recent years, with the increase of multi-polarization capability of Synthetic Aperture Radar (SAR), Polarimetric Synthetic Aperture Radar (PolSAR) provides more possibilities for building damage assessment, and the polarization-characteristic-based building damage assessment method has gradually become the focus of research. However, because of the limitations of data acquisition in PolSAR, current research mainly focuses on the L, C, X, and other limited bands. To obtain an in depth understanding of the polarization characteristics of damaged buildings in SAR images and develop the application of the polarization characteristics of damaged buildings to other bands, this study conducted a simulation experiment of Ku band polarized SAR of buildings, and performed damage assessment feature analysis using the SAR image polarization decomposition method. In this study, a scale model of real materials was built and the “microwave characteristic measurement and simulation imaging scientific experiment platform” was used to conduct SAR simulation imaging of the target buildings. The Ku band polarized SAR images before and after building damage were obtained. Then, the polarization scattering characteristics of buildings before and after damage were analyzed using various common polarization decomposition methods such as H/A/α decomposition, Yamaguchi decomposition and Touzi decomposition. Results show that the disoriented volume scattering component and the proportion of the disoriented secondary scattering component obtained by the Yamaguchi decomposition and the αs1 component obtained by the Touzi decomposition have good indicative significance for building damage assessment in the Ku band. Compared with the X band measurement results, the Ku band is more sensitive to building damage assessment, which has important implications for future radar remote sensing applications.

     

    • FullText(HTML)
    • References(25)
  • [1]
    Tadono T, Ohki M, and Abe T. Summary of natural disaster responses by the Advanced Land Observing Satellite-2(ALOS-2)[C]. The International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Kyoto, Japan, 2019: 69–72. doi: 10.5194/isprs-archives-XLII-3-W7-69-2019.
    [2]
    张风丽, 邵芸. 城市目标高分辨率SAR遥感监测技术研究进展[J]. 遥感技术与应用, 2010, 25(3): 415–422. doi: 10.11873/j.issn.1004-0323.2010.3.415

    ZHANG Fengli and SHAO Yun. Urban target monitoring using high resolution SAR data[J]. Remote Sensing Technology and Application, 2010, 25(3): 415–422. doi: 10.11873/j.issn.1004-0323.2010.3.415
    [3]
    CHEN Qihao, LI Linlin, JIANG Ping, et al. Building collapse extraction using modified freeman decomposition from post-disaster polarimetric SAR image[C]. 2016 IEEE International Geoscience And Remote Sensing Symposium (IGARSS), Beijing, China, 2016: 5769–5772. doi: 10.1109/IGARSS.2016.7730507.
    [4]
    WANG Xiaoqing, DOU Aixia, JIN Dingjian, et al. Study on the comparation of building damage extracted from different RS images acquired after 2010 M = 7.1 Yushu, Qinghai, China earthquake[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 2012: 935–938. doi: 10.1109/IGARSS.2012.6351401.
    [5]
    ZHANG Haizhen, WANG Qing, ZENG Qiming, et al. A novel approach to building collapse detection from post-seismic polarimetric SAR imagery by using optimization of polarimetric contrast enhancement[C]. 2015 IEEE International Geoscience And Remote Sensing Symposium (IGARSS), Milan, Italy, 2015: 3270–3273. doi: 10.1109/IGARSS.2015.7326516.
    [6]
    PARK S E, YAMAGUCHI Y, and KIM D J. Polarimetric SAR remote sensing of the 2011 Tohoku earthquake using ALOS/PALSAR[J]. Remote sensing of Environment, 2013, 132: 212–220. doi: 10.1016/j.rse.2013.01.018
    [7]
    ZHAI Wei, SHEN Huanfeng, HUANG Chunlin, et al. Building earthquake damage information extraction from a single post-earthquake PolSAR image[J]. Remote Sensing, 2016, 8(3): 171. doi: 10.3390/rs8030171
    [8]
    SATO M, CHEN Siwei, and SATAKE M. Polarimetric SAR analysis of tsunami damage following the March 11, 2011 East Japan earthquake[J]. Proceedings of the IEEE, 2012, 100(10): 2861–2875. doi: 10.1109/JPROC.2012.2200649
    [9]
    WATANABE M, MOTOHKA T, MIYAGI Y, et al. 2012. Analysis of urban areas affected by the 2011 off the pacific coast of Tohoku earthquake and tsunami with L-Band SAR full-polarimetric mode[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(3): 472–476. doi: 10.1109/LGRS.2011.2182030
    [10]
    ZHAI Wei and HUANG Chunlin. Fast building damage mapping using a single post-earthquake PolSAR image: A case study of the 2010 Yushu earthquake[J]. Earth, Planets and Space, 2016, 68(1): 86. doi: 10.1186/s40623-016-0469-2
    [11]
    ZHAI Wei, HUANG Chunlin, and PEI Wansheng. Two new polarimetric feature parameters for the recognition of the different kinds of buildings in earthquake-stricken areas based on entropy and eigenvalues of PolSAR decomposition[J]. Remote Sensing, 2018, 10(10): 1613. doi: 10.3390/rs10101613
    [12]
    LI Xinwu, GUO Huadong, ZHANG Lu, et al. A new approach to collapsed building extraction using RADARSAT-2 polarimetric SAR imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4): 677–681. doi: 10.1109/LGRS.2011.2178392
    [13]
    SUN Weidong, SHI Lei, YANG Jie, et al. Building collapse assessment in urban areas using texture information from postevent SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(8): 3792–3808. doi: 10.1109/JSTARS.2016.2580610
    [14]
    ZHAO Lingli, YANG Jie, LI Pingxiang, et al. Damage assessment in urban areas using post-earthquake airborne PolSAR imagery[J]. International Journal of Remote Sensing, 2013, 34(24): 8952–8966. doi: 10.1080/01431161.2013.860566
    [15]
    GE Pinglan, GOKON H, and MEGURO K. A review on synthetic aperture radar-based building damage assessment in disasters[J]. Remote Sensing of Environment, 2020, 240: 111693. doi: 10.1016/j.rse.2020.111693
    [16]
    CHEN Siwei, WANG Xuesong, and SATO M. Urban damage level mapping based on scattering mechanism investigation using fully polarimetric SAR data for the 3.11 East Japan earthquake[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12): 6919–6929. doi: 10.1109/TGRS.2016.2588325
    [17]
    王雪松, 陈思伟. 合成孔径雷达极化成像解译识别技术的进展与展望[J]. 雷达学报, 2020, 9(2): 259–276. doi: 10.12000/JR19109

    WANG Xuesong and CHEN Siwei. Polarimetric synthetic aperture radar interpretation and recognition: Advances and perspectives[J]. Journal of Radars, 2020, 9(2): 259–276. doi: 10.12000/JR19109
    [18]
    FREEMAN A and DURDEN S L. A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963–973. doi: 10.1109/36.673687
    [19]
    YAMAGUCHI Y, SATO A, BOERNER W M, et al. Four-component scattering power decomposition with rotation of coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 2251–2258. doi: 10.1109/TGRS.2010.2099124
    [20]
    JI Kefeng and WU Yonghui. Scattering mechanism extraction by a modified cloude-pottier decomposition for dual polarization SAR[J]. Remote Sensing, 2015, 7(6): 7447–7470. doi: 10.3390/rs70607447
    [21]
    CHIEN L L C, EWE H T, and SAW S H. Understanding the correlation in scattering mechanisms between H-Alpha decomposition and theoretical modelling[C]. 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama), Toyama, Japan, 2018: 1198–1203. doi: 10.23919/PIERS.2018.8597662.
    [22]
    TOUZI R. Target scattering decomposition in terms of roll-invariant target parameters[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(1): 73–84. doi: 10.1109/TGRS.2006.886176
    [23]
    AN Wentao, XIE Chunhua, YUAN Xinzhe, et al. Four-component decomposition of polarimetric SAR images with deorientation[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(6): 1090–1094. doi: 10.1109/LGRS.2011.2157078
    [24]
    孙翔, 宋红军, 王宇, 等. 基于高分辨率全极化SAR图像的取向角校正方法[J]. 雷达学报, 2018, 7(4): 465–474. doi: 10.12000/JR18026

    SUN Xiang, SONG Hongjun, WANG R, et al. POA correction method using high-resolution full-polarization SAR image[J]. Journal of Radars, 2018, 7(4): 465–474. doi: 10.12000/JR18026
    [25]
    LIU Shan, ZHANG Fengli, WEI Shiying, et al. Building damage mapping based on Touzi decomposition using quad-polarimetric ALOS PALSAR data[J]. Frontiers of Earth Science, 2020: 1–12. doi: 10.1007/s11707-019-0779-3
    • Relative Articles

  • Relative Articles

    [1]HE Zhuoyuan, CHEN Shengyao, ZHU Han, XI Feng, LI Hongtao, LIU Zhong. Transmit Waveform Design for Symbol-level Precoding-based One-bit Dual-functional Radar-communication[J]. Journal of Radars. doi: 10.12000/JR24217
    [2]WANG Xianmei, LIU Xiangbo, REN Yuzheng, LU Yang, ZHANG Haijun. Review of Research on Artificial Intelligence-Driven Joint Radar Communication[J]. Journal of Radars. doi: 10.12000/JR24252
    [3]LIU Fan, LU Shihang, CHEN Zihao. MIMO-ISAC Precoding Design Toward Random Signals[J]. Journal of Radars. doi: 10.12000/JR25019
    [4]LIU Yan, WAN Xianrong, YI Jianxin. OFDM Waveform Design for Joint Radar-communication Based on Data Distortion[J]. Journal of Radars, 2024, 13(1): 160-173. doi: 10.12000/JR23205
    [5]KONG Lingjiang, GUO Shisheng, CHEN Jiahui, WU Peilun, CUI Guolong. Overview and Prospects of Multipath Exploitation Radar Target Detection Technology[J]. Journal of Radars, 2024, 13(1): 23-45. doi: 10.12000/JR23134
    [6]LIU Liu, LIANG Xingdong, LI Yanlei, ZENG Zhiyuan, TANG Haibo. A Novel Joint Radar-communication Waveform Design Method Based on Distributed Aperture[J]. Journal of Radars, 2023, 12(2): 297-311. doi: 10.12000/JR23019
    [7]LI Wanlu, XIANG Zheng, REN Peng. Filter Bank Multi-carrier Waveform Design for Low Probability of Intercepting Joint Radar and Communication System[J]. Journal of Radars, 2023, 12(2): 287-296. doi: 10.12000/JR22064
    [8]MA Dingyou, LIU Xiang, HUANG Tianyao, LIU Yimin. Joint Radar and Communications: Shared Waveform Designs and Performance Bounds[J]. Journal of Radars, 2022, 11(2): 198-212. doi: 10.12000/JR21146
    [9]LIU Fan, YUAN Weijie, YUAN Jinhong, ZHANG J. Andrew, FEI Zesong, ZHOU Jianming. Radar-communication Spectrum Sharing and Integration: Overview and Prospect[J]. Journal of Radars, 2021, 10(3): 467-484. doi: 10.12000/JR20113
    [10]ZHAO Yuzhen, CHEN Longyong, ZHANG Fubo, LI Yanlei, WU Yirong. A New Method of Joint Radar and Communication Waveform Design and Signal Processing Based on OFDM-chirp[J]. Journal of Radars, 2021, 10(3): 453-466. doi: 10.12000/JR21028
    [11]DENG Likang, ZHANG Shuanghui, ZHANG Chi, LIU Yongxiang. A Multiple-Input Multiple-Output Inverse Synthetic Aperture Radar Imaging Method Based on Multidimensional Alternating Direction Method of Multipliers[J]. Journal of Radars, 2021, 10(3): 416-431. doi: 10.12000/JR20132
    [12]LIU Ningbo, DING Hao, HUANG Yong, DONG Yunlong, WANG Guoqing, DONG Kai. Annual Progress of the Sea-detecting X-band Radar and Data Acquisition Program[J]. Journal of Radars, 2021, 10(1): 173-182. doi: 10.12000/JR21011
    [13]SHI Longfei, QUAN Yuan, FAN Jintao, MA Jiazhi. Communicational Radar Detection Technology[J]. Journal of Radars, 2020, 9(6): 1056-1063. doi: 10.12000/JR20088
    [14]ZHENG Guimei, SONG Yuwei, HU Guoping, LI Binbin, ZHANG Dong. Height Measurement for Meter-wave MIMO Radar Based on Block Orthogonal Matching Pursuit Preprocessing[J]. Journal of Radars, 2020, 9(5): 908-915. doi: 10.12000/JR20042
    [15]DING Hao, LIU Ningbo, DONG Yunlong, CHEN Xiaolong, GUAN Jian. Overview and Prospects of Radar Sea Clutter Measurement Experiments[J]. Journal of Radars, 2019, 8(3): 281-302. doi: 10.12000/JR19006
    [16]ZENG Zheng, ZHANG Fubo, CHEN Longyong, BU Xiangxi, ZHOU Siyan. A Two-dimensional Mixed Baseline Method Based on MIMO-SAR for Countering Deceptive Jamming[J]. Journal of Radars, 2019, 8(1): 90-99. doi: 10.12000/JR18118
    [17]Zhu Kehong, Wang Jie, Liang Xingdong, Wu Yirong. Filter Bank Multicarrier Waveform Used for Integrated SAR and Communication Systems[J]. Journal of Radars, 2018, 7(5): 602-612. doi: 10.12000/JR18038
    [18]Wang Longgang, Li Lianlin. Short-range Radar Detection with (M, N)-Coprime Array Configurations(in English)[J]. Journal of Radars, 2016, 5(3): 244-253. doi: 10.12000/JR16022
    [19]Hu Cheng, Liu Changjiang, Zeng Tao. Bistatic Forward Scattering Radar Detection and Imaging[J]. Journal of Radars, 2016, 5(3): 229-243. doi: 10.12000/JR16058
    [20]Li Zi-qi, Mei Jin-jie, Hu Deng-oeng, Shen Xu-chi, Li Xiao-bai. Peak-to-Average Power Ratio Reduction for Integration of Radar and Communication Systems Based on OFDM Signals with Block Golay Coding[J]. Journal of Radars, 2014, 3(5): 548-555. doi: 10.3724/SP.J.1300.2014.14059
    • Supplements(0)
    • Cited By

  • Cited by

    Periodical cited type(3)

    1. 吴涵，吴福伟，尚士泽，杨予昊，李大圣. 太赫兹视频SAR多普勒中心估计方法. 太赫兹科学与电子信息学报. 2022(11): 1123-1129 .
    2. 孙伟峰，贾晨，李雯，王青，戴永寿. 基于C#与MATLAB混合编程的DopScat海流反演平台. 实验技术与管理. 2022(12): 129-136 .
    3. 俞传龙，王伟清. 基于时频变换的雷达运动目标参数估计方法研究. 电子设计工程. 2020(17): 122-126 .

    Other cited types(0)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-0401020304050
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 30.8 %FULLTEXT: 30.8 %META: 64.2 %META: 64.2 %PDF: 5.0 %PDF: 5.0 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 3.4 %其他: 3.4 %其他: 0.6 %其他: 0.6 %Central District: 0.0 %Central District: 0.0 %China: 0.7 %China: 0.7 %Falls Church: 0.0 %Falls Church: 0.0 %Greece: 0.0 %Greece: 0.0 %Hanoi: 0.1 %Hanoi: 0.1 %India: 0.0 %India: 0.0 %Latvia: 0.0 %Latvia: 0.0 %Matawan: 0.0 %Matawan: 0.0 %Nahant: 0.0 %Nahant: 0.0 %Saitama: 0.0 %Saitama: 0.0 %Seattle: 0.1 %Seattle: 0.1 %Taiwan, China: 0.0 %Taiwan, China: 0.0 %United States: 0.3 %United States: 0.3 %[]: 1.1 %[]: 1.1 %上海: 1.5 %上海: 1.5 %东京: 0.2 %东京: 0.2 %东京都: 0.0 %东京都: 0.0 %东莞: 0.1 %东莞: 0.1 %中卫: 0.1 %中卫: 0.1 %临汾: 0.1 %临汾: 0.1 %丹东: 0.0 %丹东: 0.0 %乌鲁木齐: 0.0 %乌鲁木齐: 0.0 %亳州: 0.1 %亳州: 0.1 %伊斯兰堡: 0.1 %伊斯兰堡: 0.1 %伦敦: 0.0 %伦敦: 0.0 %佛山: 0.1 %佛山: 0.1 %保定: 0.0 %保定: 0.0 %六安: 0.1 %六安: 0.1 %兰州: 0.1 %兰州: 0.1 %兰辛: 0.1 %兰辛: 0.1 %包头: 0.0 %包头: 0.0 %北京: 17.3 %北京: 17.3 %十堰: 0.0 %十堰: 0.0 %南京: 1.1 %南京: 1.1 %南宁: 0.0 %南宁: 0.0 %南昌: 0.1 %南昌: 0.1 %南通: 0.1 %南通: 0.1 %南阳: 0.0 %南阳: 0.0 %卡尔瓦多斯: 0.1 %卡尔瓦多斯: 0.1 %双鸭山: 0.1 %双鸭山: 0.1 %台北: 0.1 %台北: 0.1 %台州: 0.1 %台州: 0.1 %台湾省: 0.0 %台湾省: 0.0 %合肥: 0.2 %合肥: 0.2 %吉隆坡: 0.1 %吉隆坡: 0.1 %呼和浩特: 0.1 %呼和浩特: 0.1 %咸宁: 0.2 %咸宁: 0.2 %咸阳: 0.0 %咸阳: 0.0 %哥伦布: 0.1 %哥伦布: 0.1 %大连: 0.1 %大连: 0.1 %天津: 0.4 %天津: 0.4 %太原: 0.2 %太原: 0.2 %奥尔巴尼: 0.0 %奥尔巴尼: 0.0 %威海: 0.0 %威海: 0.0 %孟买: 0.1 %孟买: 0.1 %宁波: 0.0 %宁波: 0.0 %安康: 0.0 %安康: 0.0 %宣城: 0.1 %宣城: 0.1 %宫城: 0.1 %宫城: 0.1 %宫城县: 0.1 %宫城县: 0.1 %宿州: 0.0 %宿州: 0.0 %宿迁: 0.1 %宿迁: 0.1 %岳阳: 0.0 %岳阳: 0.0 %巴音郭楞: 0.0 %巴音郭楞: 0.0 %常州: 0.1 %常州: 0.1 %平顶山: 0.0 %平顶山: 0.0 %广州: 0.5 %广州: 0.5 %廊坊: 0.1 %廊坊: 0.1 %开封: 0.1 %开封: 0.1 %张家口: 0.9 %张家口: 0.9 %张家界: 0.2 %张家界: 0.2 %徐州: 0.2 %徐州: 0.2 %恩施: 0.0 %恩施: 0.0 %悉尼: 0.1 %悉尼: 0.1 %成都: 0.5 %成都: 0.5 %扬州: 0.3 %扬州: 0.3 %新乡: 0.1 %新乡: 0.1 %新布朗斯维克: 0.1 %新布朗斯维克: 0.1 %无锡: 0.1 %无锡: 0.1 %无锡市滨湖区: 0.0 %无锡市滨湖区: 0.0 %昆明: 0.2 %昆明: 0.2 %晋城: 0.1 %晋城: 0.1 %朝阳: 0.1 %朝阳: 0.1 %本溪: 0.1 %本溪: 0.1 %杜塞尔多夫: 0.0 %杜塞尔多夫: 0.0 %杭州: 0.7 %杭州: 0.7 %株洲: 0.1 %株洲: 0.1 %格兰特县: 0.1 %格兰特县: 0.1 %桂林: 0.0 %桂林: 0.0 %榆林: 0.1 %榆林: 0.1 %武汉: 0.6 %武汉: 0.6 %江门: 0.0 %江门: 0.0 %沈阳: 0.1 %沈阳: 0.1 %泰州: 0.0 %泰州: 0.0 %泰米尔纳德: 0.1 %泰米尔纳德: 0.1 %洛杉矶: 0.1 %洛杉矶: 0.1 %洛阳: 0.1 %洛阳: 0.1 %济南: 0.4 %济南: 0.4 %淮南: 0.1 %淮南: 0.1 %深圳: 0.4 %深圳: 0.4 %温州: 0.0 %温州: 0.0 %渭南: 0.1 %渭南: 0.1 %湖州: 0.4 %湖州: 0.4 %湘潭: 0.1 %湘潭: 0.1 %湛江: 0.0 %湛江: 0.0 %漯河: 0.4 %漯河: 0.4 %潍坊: 0.0 %潍坊: 0.0 %濮阳: 0.0 %濮阳: 0.0 %烟台: 0.0 %烟台: 0.0 %玉林: 0.0 %玉林: 0.0 %石家庄: 0.2 %石家庄: 0.2 %秦皇岛: 0.1 %秦皇岛: 0.1 %绵阳: 0.1 %绵阳: 0.1 %美国伊利诺斯芝加哥: 0.1 %美国伊利诺斯芝加哥: 0.1 %聊城: 0.0 %聊城: 0.0 %芒廷维尤: 15.4 %芒廷维尤: 15.4 %芝加哥: 0.3 %芝加哥: 0.3 %苏州: 0.6 %苏州: 0.6 %衡水: 0.1 %衡水: 0.1 %衢州: 0.1 %衢州: 0.1 %西宁: 40.3 %西宁: 40.3 %西安: 0.9 %西安: 0.9 %诺伊达: 0.0 %诺伊达: 0.0 %贵港: 0.1 %贵港: 0.1 %贵阳: 0.1 %贵阳: 0.1 %资阳: 0.1 %资阳: 0.1 %达州: 0.3 %达州: 0.3 %运城: 0.5 %运城: 0.5 %连云港: 0.0 %连云港: 0.0 %邢台: 0.1 %邢台: 0.1 %邯郸: 0.1 %邯郸: 0.1 %郑州: 1.0 %郑州: 1.0 %重庆: 0.2 %重庆: 0.2 %金华: 0.0 %金华: 0.0 %铁岭: 0.0 %铁岭: 0.0 %长春: 0.2 %长春: 0.2 %长沙: 1.3 %长沙: 1.3 %长治: 0.1 %长治: 0.1 %防城港: 0.0 %防城港: 0.0 %阳江: 0.0 %阳江: 0.0 %阿利坎特: 0.0 %阿利坎特: 0.0 %青岛: 0.2 %青岛: 0.2 %香港: 0.2 %香港: 0.2 %香港特别行政区: 0.1 %香港特别行政区: 0.1 %马尼拉: 0.1 %马尼拉: 0.1 %其他其他Central DistrictChinaFalls ChurchGreeceHanoiIndiaLatviaMatawanNahantSaitamaSeattleTaiwan, ChinaUnited States[]上海东京东京都东莞中卫临汾丹东乌鲁木齐亳州伊斯兰堡伦敦佛山保定六安兰州兰辛包头北京十堰南京南宁南昌南通南阳卡尔瓦多斯双鸭山台北台州台湾省合肥吉隆坡呼和浩特咸宁咸阳哥伦布大连天津太原奥尔巴尼威海孟买宁波安康宣城宫城宫城县宿州宿迁岳阳巴音郭楞常州平顶山广州廊坊开封张家口张家界徐州恩施悉尼成都扬州新乡新布朗斯维克无锡无锡市滨湖区昆明晋城朝阳本溪杜塞尔多夫杭州株洲格兰特县桂林榆林武汉江门沈阳泰州泰米尔纳德洛杉矶洛阳济南淮南深圳温州渭南湖州湘潭湛江漯河潍坊濮阳烟台玉林石家庄秦皇岛绵阳美国伊利诺斯芝加哥聊城芒廷维尤芝加哥苏州衡水衢州西宁西安诺伊达贵港贵阳资阳达州运城连云港邢台邯郸郑州重庆金华铁岭长春长沙长治防城港阳江阿利坎特青岛香港香港特别行政区马尼拉

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(2891) PDF downloads(242) Cited by(3)
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return