Volume 5 Issue 6
Jan.  2017
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2016  >  5(6): 692-700
Sun Xun, Huang Pingping, Tu Shangtan, Yang Xiangli. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning[J]. Journal of Radars, 2016, 5(6): 692-700. doi: 10.12000/JR15132
Citation: Sun Xun, Huang Pingping, Tu Shangtan, Yang Xiangli. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning[J]. Journal of Radars, 2016, 5(6): 692-700. doi: 10.12000/JR15132
shu

Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

doi: 10.12000/JR15132
  • 1.

    (School of Electronic Information, Wuhan University, Wuhan 430072, China)

  • 2.

    (Radar Research Institute, Inner Mongolia University of Technology, Hohhot 010051, China)

  • 3.

    (Shanghai Institute of Satellite Engineering, Shanghai 200240, China)

Funds:

The Inner Mongolia Autonomous Region Science and Technology Project (20131108,20140155),TheNational Natural Science Foundation of China (61271401,41501414),The Fudan University Key Laboratory of EMWInformation Open Fund Project (EMW201504)

  • Received Date: 2015-12-27
  • Rev Recd Date: 2016-04-07
  • Publish Date: 2016-12-28
    Abstract
  • In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR) images using multiple-feature fusion and ensemble learning.First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images.Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results.Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results.Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.

     

    • FullText(HTML)
  • loading
    • References(21)
  • [1]
    李春升,杨威,王鹏波.星载SAR成像处理算法综述[J].雷达学报,2013,2(1):111-122.Li Chun-sheng,Yang Wei,and Wang Peng-bo.A review of spaceborne SAR algorithm for image formation[J].Journal of Radars,2013,2(1):111-122.
    [2]
    田维,徐旭,卞小林,等.环境一号C卫星SAR图像典型环境遥感应用初探[J].雷达学报,2014,3(3):339-351.Tian Wei,Xu Xu,Bian Xiao-lin,et al..Applications of environmental remote sensing by HJ-1C SAR imagery[J].Journal of Radars,2014,3(3):339-351.
    [3]
    Yang Wen,Yin Xiao-shuang,Song Hui,et al..Extraction of built-up areas from fully polarimetric SAR imagery via PU learning[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(4):1207-1216.
    [4]
    Banerjee B,Bhattacharya A,and Buddhiraju K M.A generic land-cover classification framework for polarimetric SAR images using the optimum Touzi decomposition parameter subset-An insight on mutual information-based feature selection techniques[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(4):1167-1176.
    [5]
    Hariharan S,Tirodkar S,De S,et al..Variable importance and random forest classification using RADARSAT-2 PolSAR data[C].IEEE International Geoscience and Remote Sensing Symposium (IGARSS),Quebec,2014:1210-1213.
    [6]
    滑文强,王爽,侯彪.基于半监督学习的SVM-Wishart极化SAR图像分类方法[J].雷达学报,2015,4(1):93-98.Hua Wen-qiang,Wang Shuang,and Hou Biao.Semi-supervised Learning for classification of polarimetric SAR images based on SVM-Wishart[J].Journal of Radars,2015,4(1):93-98.
    [7]
    Yang Wen,Yin Xiao-shuang,and Xia Gui-song.Learning high-level features for satellite image classification with limited training samples[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(8):4472-4482.
    [8]
    Yuan Han-ning,Fang Meng,and Zhu Xing-quan.Hierarchical sampling for multi-instance ensemble learning[J].Knowledge and Data Engineering,2013,25(12):2900-2905.
    [9]
    Zhang La-mei,Wang Xiao,Li Meng,et al..Classification of fully polarimetric SAR images based on ensemble learning and feature integration[C].IEEE International Geoscience and Remote Sensing Symposium (IGARSS),Quebec,2014:2758-2761.
    [10]
    刘培,杜培军,谭琨.一种基于集成学习和特征融合的遥感影像分类新方法[J].红外与毫米波学报,2014,33(3):311-317.Liu Pei,Du Pei-jun,and Tan Kun.A novel remotely sensed image classification based on ensemble learning and feature integration[J].Journal of Infrared and Millimeter Waves,2014,33(3):311-317.
    [11]
    Samat A,Du P,Baig M H A,et al..Ensemble learning with multiple classifiers and polarimetric features for polarized SAR image classification[J].Photogrammetric EngineeringRemote Sensing,2014,80(3):239-251.
    [12]
    Wang Y,Zhang Y,Zhuo T,et al..Ensemble learning based on multi-features fusion and selection for polarimetric SAR image classification[C].International Conference on Signal Processing (ICSP),Hangzhou,2014:734-737.
    [13]
    Doulgeris A P.A simple and extendable segmentation method for multi-polarisation SAR images[C].6th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry,2013.
    [14]
    Yang J,Peng Y N,Yamaguchi Y,et al..On Huynen's decomposition of a Kennaugh matrix[J].IEEE Geoscience and Remote Sensing Letters,2006,3(3):369-372.
    [15]
    Hoekman D H and Vissers M A M.A new polarimetric classification approach evaluated for agricultural crops[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(12):2881-2889.
    [16]
    Cloude S R and Pottier E.An entropy based classification scheme for land applications of polarimetric SAR[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(1):68-78.
    [17]
    Yamaguchi Y,Sato A,Boerner W,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.
    [18]
    Sato A,Yamaguchi Y,Singh G,et al..Four-component scattering power decomposition with extended volume scattering model[J].IEEE Geoscience and Remote Sensing Letters,2012,9(2):166-170.
    [19]
    Zhang Cha and Ma Yun-qian.Ensemble Machine Learning[M].Springer US,2012:1-34.
    [20]
    陈博,王爽,焦李成,等.利用0-1矩阵分解集成的极化SAR图像分类[J].电子与信息学报,2015,37(6):1495-1501.Chen Bo,Wang Shuang,Jiao Li-cheng,et al..Polarimetric SAR image classification via weighted ensemble based on 0-1 matrix decomposition[J].Journal of ElectronicsInformation Technology,2015,37(6):1495-1501.
    [21]
    Chang Chih-Chung and Lin Chih-Jen.Libsvm:A library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology,2011,2(3):27.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

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(3286) PDF downloads(1516) Cited by()
    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