LI Peng, LI Dahui, LI Zhenhong, WANG Houjie. Wetland Classification Through Integration of GF-3 SAR and Sentinel-2B Multispectral Data over the Yellow River Delta[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1641-1649. DOI: 10.13203/j.whugis20180258
Citation: LI Peng, LI Dahui, LI Zhenhong, WANG Houjie. Wetland Classification Through Integration of GF-3 SAR and Sentinel-2B Multispectral Data over the Yellow River Delta[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1641-1649. DOI: 10.13203/j.whugis20180258

Wetland Classification Through Integration of GF-3 SAR and Sentinel-2B Multispectral Data over the Yellow River Delta

Funds: 

The National Natural Science Foundation of China 41806108

National Key Research and Development Program of China 2017YFE0133500

National Key Research and Development Program of China 2016YFA0600903

Shandong Provincial Natural Science Foundation ZR2016DB30

China Postdoctoral Science Foundation 2016M592248

Qingdao Indigenous Innovation Program 16-5-1-25-jch

Fundamental Research Funds for the Central Universities 201713039

Qingdao Postdoctoral Application Research Project 

More Information
  • Author Bio:

    LI Peng, PhD, lecturer, specializes in remote sensing of coastal environment. E-mail:pengli@ouc.edu.cn

  • Corresponding author:

    LI Zhenhong, professor, PhD. E-mail:zhenhong.li@newcastle.ac.uk

  • Received Date: November 03, 2018
  • Published Date: November 04, 2019
  • It is of great significance to monitor dynamic change of wetland over the Yellow River Delta for rational utilization, development and protection of wetland resources. Both Gaofen-3 (GF-3) SAR data and Sentinel-2B multispectral data were used to analyze the spectral, index, polarization scatter and texture feature information of seven types of ground objects over the Yellow River Delta wetland, and then supervised classification was implemented with maximum likelihood (ML), decision tree (DT) and support vector machine (SVM) classifier. The performances of both the joint and the individual classifications with GF-3 and Sentinel-2B data were also evaluated. The results of three algorithms show that the overall accuracy of the joint classification can reach 90.4%, 95.4%, 95.7%, significantly higher than that of the individual classifications, showing the promising potential of GF-3 SAR and Sentinel-2B multi-spectral images in joint wetland classification.
  • [1]
    韩美, 张晓惠, 刘丽云.黄河三角洲湿地研究进展[J].生态环境学报, 2006, 15(4):872-875 doi: 10.3969/j.issn.1674-5906.2006.04.041

    Han Mei, Zhang Xiaohui, Liu Liyun. Research Progress on Wetland of the Yellow River Delta[J]. Ecology and Enviroment, 2006, 15(4):872-875 doi: 10.3969/j.issn.1674-5906.2006.04.041
    [2]
    肖笃宁, 韩慕康, 李晓文, 等.环渤海海平面上升与三角洲湿地保护[J].第四纪研究, 2003, 23(3):237-246 doi: 10.3321/j.issn:1001-7410.2003.03.001

    Xiao Duning, Han Mukang, Li Xiaowen, et al. Sea Level Rising Around Bohai Sea and Deltaic Wetlands Protection[J]. Quaternary Sciences, 2003, 23(3):237-246 doi: 10.3321/j.issn:1001-7410.2003.03.001
    [3]
    修长军, 王晓慧.胜利油田开发对黄河三角洲湿地的环境影响及环境管理[J].中国环境管理, 2003(3):59-60 http://www.cnki.com.cn/Article/CJFDTotal-HJGN200303026.htm

    Xiu Changjun, Wang Xiaohui. Environmental Impact and Management for the Yellow River Delta Swamp with Shengli Oilfield Development[J]. China environmental Management, 2003(3):59-60 http://www.cnki.com.cn/Article/CJFDTotal-HJGN200303026.htm
    [4]
    孙家抦.遥感原理与应用[M]. 3版.武汉:武汉大学出版社, 2009

    Sun Jiabing. Principles and Applications of Remote Sensing[M]. 3rd ed.Wuhan: Wuhan University Press, 2009
    [5]
    张海龙, 蒋建军, 吴宏安, 等. SAR与TM影像融合及在BP神经网络分类中的应用[J].测绘学报, 2006, 35(3):229-233 doi: 10.3321/j.issn:1001-1595.2006.03.007

    Zhang Hailong, Jiang Jianjun, Wu Hong'an, et al. The BP Neural Network Classification Based on the Fusion of SAR and TM Images[J]. Acta Geodaetica et Cartographica Sinica, 2006, 35(3):229-233 doi: 10.3321/j.issn:1001-1595.2006.03.007
    [6]
    贾坤, 李强子, 田亦陈, 等.微波后向散射数据改进农作物光谱分类精度研究[J].光谱学与光谱分析, 2011, 31(2):483-487 doi: 10.3964/j.issn.1000-0593(2011)02-0483-05

    Jia Kun, Li Qiangzi, Tian Yichen, et al. Accuracy Improvement of Spectral Classification of Crop Using Microwave Backscatter Data[J]. Spectroscopy and Spectral Analysis, 2011, 31(2):483-487 doi: 10.3964/j.issn.1000-0593(2011)02-0483-05
    [7]
    Gao H, Wang C C, Wang G Y, et al. A Crop Classification Method Integrating GF-3 PolSAR and Sentinel-2A Optical Data in the Dongting Lake Basin[J]. Sensors, 2018, 18(9):1-19 doi: 10.1109/JSEN.2018.2815438
    [8]
    张磊, 宫兆宁, 王启为, 等. Sentinel-2影像多特征优选的黄河三角洲湿地信息提取[J].遥感学报, 2019, 23(2):313-326 http://d.old.wanfangdata.com.cn/Periodical/ygxb201902012

    Zhang Lei, Gong Zhaoning, Wang Qiwei, et al. Wetland Mapping of Yellow River Delta Wetlands Based on Multi-feature Optimization of Sentinel-2 Images[J]. Journal of Remote Sensing, 2009, 23(2):313-326 http://d.old.wanfangdata.com.cn/Periodical/ygxb201902012
    [9]
    Buono A, Nunziata F, Migliaccio M, et al. Classification of the Yellow River Delta Area Using Fully Polarimetric SAR Measurements[J]. International Journal of Remote Sensing, 2017, 38(23):6 714-6 734 doi: 10.1080/01431161.2017.1363437
    [10]
    Gou S P, Li X F, Yang X F. Coastal Zone Classification with Fully Polarimetric SAR Imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(11):1 616-1 620 doi: 10.1109/LGRS.2016.2597965
    [11]
    Chang Y L, Li P X, Yang J, et al. Polarimetric Calibration and Quality Assessment of the GF-3 Satellite Images[J]. Sensors, 2018, 18(2):1-12 http://cn.bing.com/academic/profile?id=c7d850ba802d9abef21206fa00f20607&encoded=0&v=paper_preview&mkt=zh-cn
    [12]
    张庆君.高分三号卫星总体设计与关键技术[J].测绘学报, 2017, 46(3):269-277 http://d.old.wanfangdata.com.cn/Periodical/chxb201703002

    Zhang Qingjun.System Design and Key Technologies of the GF-3 Satellite[J].Acta Geodaetica et Cartographica Sinica, 2017, 46(3):269-277 http://d.old.wanfangdata.com.cn/Periodical/chxb201703002
    [13]
    Yin Junjun, Yang Jian, Zhang Qingjun. Assessment of GF-3 Polarimetric SAR Data for Physical Scattering Mechanism Analysis and Terrain Classification[J]. Sensors, 2017, 17(12):1-11 doi: 10.1109/JSEN.2017.2693641
    [14]
    郭健, 张继贤, 张永红, 等.多时相MODIS影像土地覆盖分类比较研究[J].测绘学报, 2009, 38(12):88-92 http://d.old.wanfangdata.com.cn/Periodical/chxb200901015

    Guo Jian, Zhang Jixian, Zhang Yonghong, at al. Study of the Comparison of Land Cover Classification for Multitemporal MODIS Images[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(12):88-92 http://d.old.wanfangdata.com.cn/Periodical/chxb200901015
    [15]
    Vapnik V N. An Overview of Statistical Learning Theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5):988-999 doi: 10.1109/72.788640
    [16]
    吴永辉, 计科峰, 郁文贤.利用SVM的全极化、双极化与单极化SAR图像分类性能的比较[J].遥感学报, 2008, 12(1):46-53 http://d.old.wanfangdata.com.cn/Periodical/ygxb200801007

    Wu Yonghui, Ji Kefeng, Yu Wenxian. Comparison of Classification Porfermance of Full-, Dual- and Single-Polarization SAR Images Using SVM[J]. Journal of Remote Sensing, 2008, 12(1):46-53 http://d.old.wanfangdata.com.cn/Periodical/ygxb200801007
    [17]
    黄瑾.基于多波段多极化SAR数据的黄河口湿地分类研究[D].青岛: 中国石油大学(华东), 2011 http://d.wanfangdata.com.cn/Thesis/Y1875773

    Huang Jin. Classification of the Yellow River Estuary Wetland Based on Multiband and Multipolarization SAR Data[D]. Qingdao: China University of Petroleum(East China), 2011 http://d.wanfangdata.com.cn/Thesis/Y1875773
    [18]
    赵泉华, 郭世波, 李晓丽, 等.利用目标分解特征的全极化SAR海冰分类[J].测绘学报, 2018, 47(12):1 609-1 620 doi: 10.11947/j.AGCS.2018.20170551

    Zhao Quanhua, Guo Shibo, Li Xiaoli, et al. Polarimetric SAR Sea Ice Classification Based on Target Decompositional Features[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(12):1 609-1 620 doi: 10.11947/j.AGCS.2018.20170551
    [19]
    Freeman A, Durden S L. A Three-component Scattering Model for Polarimetric SAR Data[J]. IEEE Transactions on Geoscience Remote Sensing, 1998, 36(3): 963-973 doi: 10.1109/36.673687
    [20]
    刘修国, 黄晓东, 陈启浩, 等.归一化法消除全极化SAR影像Freeman分解中的不一致[J].武汉大学学报·信息科学版, 2013, 38(3):257-261 http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb201303002

    Liu Xiuguo, Huang Xiaodong, Chen Qihao, et al. Inconsistency Elimination in Freeman Decomposition for Polarimetric SAR Data on Normalization Method[J]. Geomatics and Information Science of Wuhan University, 2013, 38(3):257-261 http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb201303002
    [21]
    李粉玲, 常庆瑞, 刘佳岐, 等.基于多纹理和支持向量机的ZY-1 02C星HR数据分类[J].武汉大学学报∙信息科学版, 2016, 41(4):455-461, 486 http://ch.whu.edu.cn/CN/abstract/abstract5414.shtml

    Li Fenling, Chang Qingrui, Liu Jiaqi, et al. SVM Classification with Multi-texture Data of ZY-1 02C HR Image[J]. Geomatics and Information Science of Wuhan University, 2016, 41(3):455-461, 486 http://ch.whu.edu.cn/CN/abstract/abstract5414.shtml
    [22]
    侯飞, 胡召玲.地形信息辅助下的全极化SAR数据土地覆盖分类[J].测绘通报, 2016(3):40-43 http://d.old.wanfangdata.com.cn/Periodical/chtb201603011

    Hou Fei, Hu Zhaoling. Land Cover Classification Based on Fully Polarmetric SAR Data Supported by Topography Information[J]. Bulletin of Surveying and Mapping, 2016(3):40-43 http://d.old.wanfangdata.com.cn/Periodical/chtb201603011
    [23]
    王春艳, 刘佳新, 徐爱功, 等.一种新的高分辨率遥感影像模糊监督分类方法[J].武汉大学学报·信息科学版, 2018, 43(6): 922-929 http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb201806017

    Wang Chunyan, Liu Jiaxin, Xu Aigong, et al. A New Method of Fuzzy Supervised Classification of High Resolution Remote Sensing Image[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6):922-929 http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb201806017
  • Related Articles

    [1]MA Jingzhen, SUN Qun, WEN Bowei, ZHOU Zhao, LU Chuanwei, LÜ Zheng, SUN Shijie. A Hybrid Multi-feature Road Network Selection Method Based on Trajectory Data[J]. Geomatics and Information Science of Wuhan University, 2022, 47(7): 1009-1016. DOI: 10.13203/j.whugis20190480
    [2]YANG Hao, HE Zongyi, CHEN Huayang, ZHOU Zhuanxiang, FAN Yong. A Method for Automatic Generalization of Urban Settlements Considering Road Network[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 965-970. DOI: 10.13203/j.whugis20160094
    [3]CAO Weiwei, ZHANG Hong, HE Jing, LAN Tian. Road Selection Considering Structural and Geometric Properties[J]. Geomatics and Information Science of Wuhan University, 2017, 42(4): 520-524. DOI: 10.13203/j.whugis20140862
    [4]YANG Lin, WAN Bo, WANG Run, ZUO Zejun, AN Xiaoya. Matching Road Network Based on the Structural Relationship Constraint of Hierarchical Strokes[J]. Geomatics and Information Science of Wuhan University, 2015, 40(12): 1661-1668. DOI: 10.13203/j.whugis20140295
    [5]tianjin g, renchan g, wangyihen g, xiongfu q uan, leiyin g zhe. imp rovementofself-best-fitstrate gyforstrokebuildin g[J]. Geomatics and Information Science of Wuhan University, 2015, 40(9): 1209-1214. DOI: 10.13203/j .whu g is20140455
    [6]LIU Hailong, QIAN Haizhong, WANG Xiao, HE Haiwei. Road Networks Global Matching Method Using Analytical Hierarchy Process[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5): 644-651. DOI: 10.13203/j.whugis20130350
    [7]TIAN Jing, HE Qingsong, YAN Fen. Formalization and New Algorithm of stroke Generation in Road Networks[J]. Geomatics and Information Science of Wuhan University, 2014, 39(5): 556-560. DOI: 10.13203/j.whugis20120127
    [8]TIAN Jing, WU Dang, ZHAN Yifei. Degree Correlation of Urban Street Networks[J]. Geomatics and Information Science of Wuhan University, 2014, 39(3): 332-334. DOI: 10.13203/j.whugis20120675
    [9]CHEN Jun, HU Yungang, ZHAO Renliang, LI Zhilin. Road Data Updating Based on Map Generalization[J]. Geomatics and Information Science of Wuhan University, 2007, 32(11): 1022-1027.
    [10]HUANG Shuqiang, SUN Chengzhi, FU Zhongliang. License Plate Binarization Algorithm Based on the Features of Characters' Strokes[J]. Geomatics and Information Science of Wuhan University, 2003, 28(1): 71-73,79.
  • Cited by

    Periodical cited type(9)

    1. 赵天明,孙群,马京振,张付兵,温伯威. 融合路段和stroke特征的道路自动选取方法. 地球信息科学学报. 2024(12): 2673-2685 .
    2. 郭漩,钱海忠,王骁,刘俊楠,任琰,赵钰哲,陈国庆. 多源道路智能选取的本体知识推理方法. 测绘学报. 2022(02): 279-289 .
    3. 马京振,孙群,温伯威,周炤,陆川伟,吕峥,孙士杰. 结合轨迹数据的混合多特征道路网选取方法. 武汉大学学报(信息科学版). 2022(07): 1009-1016 .
    4. 朱余德,杨敏,晏雄锋. 利用图卷积神经网络的道路网选取方法. 北京测绘. 2022(11): 1455-1459 .
    5. 韩远,王中辉,徐智邦,余贝贝. 结合引力场理论的道路自动选取方法. 测绘科学. 2021(01): 189-195 .
    6. 韩远,王中辉,禄小敏. POI辅助下的道路选取. 测绘科学. 2021(04): 165-171 .
    7. 陈晓东,余劲松弟. 顾及语义关联信息的道路选取方法. 海南大学学报(自然科学版). 2021(03): 227-234 .
    8. 王晓妍. 土地利用图中线状要素综合的质量评价. 测绘通报. 2020(04): 116-120 .
    9. 冯云,朱素华,孙益清,王金鑫. 郑州轨道交通5号线开通对城市交通格局的影响. 城市勘测. 2020(04): 54-58 .

    Other cited types(11)

Catalog

    Article views PDF downloads Cited by(20)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return