Volume 5 Issue 6
Jan.  2017
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Article Navigation >  Journal of Radars  > 2016  >  5(6): 692-700
DING Zihang, XIE Junwei, and WANG Bo. Missing covariance matrix recovery with the FDA-MIMO radar using deep learning method[J]. Journal of Radars, 2023, 12(5): 1112–1124. doi: 10.12000/JR23002
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.

     

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    • References(21)
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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 6.5 %其他: 6.5 %其他: 1.7 %其他: 1.7 %China: 0.3 %China: 0.3 %Falls Church: 0.0 %Falls Church: 0.0 %Happy Valley: 0.1 %Happy Valley: 0.1 %Kao-sung: 0.1 %Kao-sung: 0.1 %Tottori-shi: 0.2 %Tottori-shi: 0.2 %三亚: 0.0 %三亚: 0.0 %上海: 1.4 %上海: 1.4 %东京: 0.3 %东京: 0.3 %东京都: 0.0 %东京都: 0.0 %东莞: 0.2 %东莞: 0.2 %中卫: 0.2 %中卫: 0.2 %丹东: 0.2 %丹东: 0.2 %乌鲁木齐: 0.0 %乌鲁木齐: 0.0 %九江: 0.0 %九江: 0.0 %九龙城: 0.0 %九龙城: 0.0 %云浮: 0.2 %云浮: 0.2 %京畿道: 0.1 %京畿道: 0.1 %佛山: 0.1 %佛山: 0.1 %六安: 0.0 %六安: 0.0 %兰州: 0.1 %兰州: 0.1 %兰辛: 0.1 %兰辛: 0.1 %内江: 0.1 %内江: 0.1 %北京: 12.2 %北京: 12.2 %十堰: 0.2 %十堰: 0.2 %华沙: 0.3 %华沙: 0.3 %南京: 3.4 %南京: 3.4 %南充: 0.0 %南充: 0.0 %南宁: 0.0 %南宁: 0.0 %南昌: 0.3 %南昌: 0.3 %卡拉奇: 0.0 %卡拉奇: 0.0 %印多尔: 0.1 %印多尔: 0.1 %厦门: 0.0 %厦门: 0.0 %台北: 0.0 %台北: 0.0 %合肥: 0.6 %合肥: 0.6 %吉林: 0.0 %吉林: 0.0 %周口: 0.0 %周口: 0.0 %呼和浩特: 0.1 %呼和浩特: 0.1 %咸阳: 0.0 %咸阳: 0.0 %哈尔滨: 0.2 %哈尔滨: 0.2 %哥伦布: 0.0 %哥伦布: 0.0 %商洛: 0.0 %商洛: 0.0 %喀什: 0.6 %喀什: 0.6 %嘉兴: 0.3 %嘉兴: 0.3 %塔斯卡卢萨: 0.0 %塔斯卡卢萨: 0.0 %大连: 0.1 %大连: 0.1 %天津: 1.2 %天津: 1.2 %太原: 0.0 %太原: 0.0 %威海: 0.0 %威海: 0.0 %娄底: 0.0 %娄底: 0.0 %孟买: 0.1 %孟买: 0.1 %安康: 0.3 %安康: 0.3 %安阳: 0.0 %安阳: 0.0 %宣城: 0.2 %宣城: 0.2 %巴中: 0.0 %巴中: 0.0 %帕萨迪纳: 0.0 %帕萨迪纳: 0.0 %常州: 0.3 %常州: 0.3 %常德: 0.5 %常德: 0.5 %平顶山: 0.0 %平顶山: 0.0 %广州: 1.8 %广州: 1.8 %库比蒂诺: 0.0 %库比蒂诺: 0.0 %延安: 0.2 %延安: 0.2 %开封: 0.6 %开封: 0.6 %弗吉: 0.1 %弗吉: 0.1 %张家口: 3.0 %张家口: 3.0 %张家界: 0.2 %张家界: 0.2 %徐州: 0.2 %徐州: 0.2 %悉尼: 0.1 %悉尼: 0.1 %惠州: 0.1 %惠州: 0.1 %成都: 2.3 %成都: 2.3 %扬州: 0.4 %扬州: 0.4 %新德里: 0.1 %新德里: 0.1 %无锡: 0.2 %无锡: 0.2 %昆明: 1.8 %昆明: 1.8 %晋城: 0.0 %晋城: 0.0 %朝阳: 0.3 %朝阳: 0.3 %来宾: 0.0 %来宾: 0.0 %杭州: 0.9 %杭州: 0.9 %松原: 0.0 %松原: 0.0 %武汉: 1.2 %武汉: 1.2 %汕头: 0.2 %汕头: 0.2 %沈阳: 0.3 %沈阳: 0.3 %河池: 0.0 %河池: 0.0 %泉州: 0.2 %泉州: 0.2 %洛阳: 0.3 %洛阳: 0.3 %济南: 0.0 %济南: 0.0 %海东: 0.1 %海东: 0.1 %海口: 0.0 %海口: 0.0 %淄博: 0.2 %淄博: 0.2 %淮南: 0.0 %淮南: 0.0 %深圳: 1.3 %深圳: 1.3 %温州: 0.3 %温州: 0.3 %湖州: 0.0 %湖州: 0.0 %漯河: 0.9 %漯河: 0.9 %漳州: 0.0 %漳州: 0.0 %潍坊: 0.2 %潍坊: 0.2 %烟台: 0.1 %烟台: 0.1 %焦作: 0.0 %焦作: 0.0 %珠海: 0.0 %珠海: 0.0 %白山: 0.0 %白山: 0.0 %眉山: 0.0 %眉山: 0.0 %石家庄: 0.6 %石家庄: 0.6 %福州: 0.1 %福州: 0.1 %秦皇岛: 0.0 %秦皇岛: 0.0 %纽约: 0.0 %纽约: 0.0 %绍兴: 0.2 %绍兴: 0.2 %绵阳: 0.0 %绵阳: 0.0 %芒廷维尤: 24.1 %芒廷维尤: 24.1 %芝加哥: 0.5 %芝加哥: 0.5 %苏州: 0.1 %苏州: 0.1 %莫斯科: 0.2 %莫斯科: 0.2 %菏泽: 0.2 %菏泽: 0.2 %葫芦岛: 0.0 %葫芦岛: 0.0 %衡水: 0.3 %衡水: 0.3 %衡阳: 1.0 %衡阳: 1.0 %衢州: 1.5 %衢州: 1.5 %襄阳: 0.0 %襄阳: 0.0 %西宁: 8.9 %西宁: 8.9 %西安: 4.2 %西安: 4.2 %诺沃克: 0.3 %诺沃克: 0.3 %贵阳: 0.1 %贵阳: 0.1 %赣州: 0.0 %赣州: 0.0 %达州: 0.1 %达州: 0.1 %迈阿密: 0.0 %迈阿密: 0.0 %运城: 0.4 %运城: 0.4 %邯郸: 0.1 %邯郸: 0.1 %郑州: 0.3 %郑州: 0.3 %鄂州: 0.0 %鄂州: 0.0 %重庆: 0.6 %重庆: 0.6 %钱德勒: 0.0 %钱德勒: 0.0 %铁岭: 0.0 %铁岭: 0.0 %银川: 0.0 %银川: 0.0 %长春: 0.3 %长春: 0.3 %长沙: 2.1 %长沙: 2.1 %阿什本: 0.0 %阿什本: 0.0 %青岛: 0.6 %青岛: 0.6 %首尔: 0.0 %首尔: 0.0 %首尔特别: 0.1 %首尔特别: 0.1 %香港: 0.3 %香港: 0.3 %马鞍山: 0.0 %马鞍山: 0.0 %黄冈: 0.0 %黄冈: 0.0 %齐齐哈尔: 0.6 %齐齐哈尔: 0.6 %其他其他ChinaFalls ChurchHappy ValleyKao-sungTottori-shi三亚上海东京东京都东莞中卫丹东乌鲁木齐九江九龙城云浮京畿道佛山六安兰州兰辛内江北京十堰华沙南京南充南宁南昌卡拉奇印多尔厦门台北合肥吉林周口呼和浩特咸阳哈尔滨哥伦布商洛喀什嘉兴塔斯卡卢萨大连天津太原威海娄底孟买安康安阳宣城巴中帕萨迪纳常州常德平顶山广州库比蒂诺延安开封弗吉张家口张家界徐州悉尼惠州成都扬州新德里无锡昆明晋城朝阳来宾杭州松原武汉汕头沈阳河池泉州洛阳济南海东海口淄博淮南深圳温州湖州漯河漳州潍坊烟台焦作珠海白山眉山石家庄福州秦皇岛纽约绍兴绵阳芒廷维尤芝加哥苏州莫斯科菏泽葫芦岛衡水衡阳衢州襄阳西宁西安诺沃克贵阳赣州达州迈阿密运城邯郸郑州鄂州重庆钱德勒铁岭银川长春长沙阿什本青岛首尔首尔特别香港马鞍山黄冈齐齐哈尔

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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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