Volume 4 Issue 6
Dec.  2015
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Article Navigation >  Journal of Radars  > 2015  >  4(6): 658-665
Wang Lu, Zhang Fan, Li Wei, Xie Xiao-ming, Hu Wei. A Method of SAR Target Recognition Based on Gabor Filter and Local Texture Feature Extraction[J]. Journal of Radars, 2015, 4(6): 658-665. doi: 10.12000/JR15076
Citation: Wang Lu, Zhang Fan, Li Wei, Xie Xiao-ming, Hu Wei. A Method of SAR Target Recognition Based on Gabor Filter and Local Texture Feature Extraction[J]. Journal of Radars, 2015, 4(6): 658-665. doi: 10.12000/JR15076
shu

A Method of SAR Target Recognition Based on Gabor Filter and Local Texture Feature Extraction

DOI: 10.12000/JR15076

  • (College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China)

Funds:

The National Natural Science Foundation of China (61302164), The Fundamental Research Funds for the Central Universities (YS1404), The Beijing Higher Education Young Elite Teacher Project (YETP0500)

  • Received Date: 2015-06-17
  • Rev Recd Date: 2015-10-16
  • Publish Date: 2015-12-28
    Abstract
  • This paper presents a novel texture feature extraction method based on a Gabor filter and Three-Patch Local Binary Patterns (TPLBP) for Synthetic Aperture Rader (SAR) target recognition. First, SAR images are processed by a Gabor filter in different directions to enhance the significant features of the targets and their shadows. Then, the effective local texture features based on the Gabor filtered images are extracted by TPLBP. This not only overcomes the shortcoming of Local Binary Patterns (LBP), which cannot describe texture features for large scale neighborhoods, but also maintains the rotation invariant characteristic which alleviates the impact of the direction variations of SAR targets on recognition performance. Finally, we use an Extreme Learning Machine (ELM) classifier and extract the texture features. The experimental results of MSTAR database demonstrate the effectiveness of the proposed method.

     

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    • References(15)
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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 16.2 %其他: 16.2 %其他: 1.2 %其他: 1.2 %Baden: 0.0 %Baden: 0.0 %Central District: 0.2 %Central District: 0.2 %China: 0.7 %China: 0.7 %Hanoi: 0.1 %Hanoi: 0.1 %Hungary: 0.2 %Hungary: 0.2 %India: 0.1 %India: 0.1 %Iran (ISLAMIC Republic Of): 0.1 %Iran (ISLAMIC Republic Of): 0.1 %Kao-sung: 0.0 %Kao-sung: 0.0 %Kennedy Town: 0.0 %Kennedy Town: 0.0 %Korea Republic of: 0.1 %Korea Republic of: 0.1 %Mexico: 0.1 %Mexico: 0.1 %Seattle: 0.0 %Seattle: 0.0 %Spain: 0.1 %Spain: 0.1 %Thailand: 0.1 %Thailand: 0.1 %United Kingdom: 0.0 %United Kingdom: 0.0 %United States: 0.3 %United States: 0.3 %Wixom: 0.1 %Wixom: 0.1 %[]: 1.2 %[]: 1.2 %上海: 2.0 %上海: 2.0 %东莞: 0.2 %东莞: 0.2 %中卫: 0.1 %中卫: 0.1 %临沂: 0.0 %临沂: 0.0 %乌兰察布: 0.0 %乌兰察布: 0.0 %佛山: 0.0 %佛山: 0.0 %保定: 0.1 %保定: 0.1 %兰州: 0.0 %兰州: 0.0 %内江: 0.0 %内江: 0.0 %加利福尼亚: 0.0 %加利福尼亚: 0.0 %包头: 0.0 %包头: 0.0 %北京: 10.7 %北京: 10.7 %北海: 0.1 %北海: 0.1 %十堰: 0.0 %十堰: 0.0 %南京: 0.9 %南京: 0.9 %南宁: 0.0 %南宁: 0.0 %南昌: 0.1 %南昌: 0.1 %厦门: 0.2 %厦门: 0.2 %古吉拉特: 0.1 %古吉拉特: 0.1 %台北: 0.2 %台北: 0.2 %台州: 0.0 %台州: 0.0 %合肥: 0.3 %合肥: 0.3 %呼和浩特: 0.2 %呼和浩特: 0.2 %哈尔滨: 0.2 %哈尔滨: 0.2 %商丘: 0.1 %商丘: 0.1 %嘉兴: 0.1 %嘉兴: 0.1 %大连: 0.2 %大连: 0.2 %天津: 0.7 %天津: 0.7 %威海: 0.1 %威海: 0.1 %安科纳: 0.1 %安科纳: 0.1 %宜宾: 0.1 %宜宾: 0.1 %宜春: 0.0 %宜春: 0.0 %宣城: 0.0 %宣城: 0.0 %宿迁: 0.1 %宿迁: 0.1 %巴音郭楞: 0.1 %巴音郭楞: 0.1 %常州: 0.1 %常州: 0.1 %常德: 0.0 %常德: 0.0 %平凉: 0.1 %平凉: 0.1 %平顶山: 0.0 %平顶山: 0.0 %广州: 0.7 %广州: 0.7 %库比蒂诺: 0.0 %库比蒂诺: 0.0 %廊坊: 0.1 %廊坊: 0.1 %延安: 0.0 %延安: 0.0 %开封: 0.1 %开封: 0.1 %张家口: 0.8 %张家口: 0.8 %张家口市: 0.0 %张家口市: 0.0 %徐州: 0.0 %徐州: 0.0 %德里久尔: 0.2 %德里久尔: 0.2 %意大利: 0.1 %意大利: 0.1 %成都: 0.5 %成都: 0.5 %扬州: 0.1 %扬州: 0.1 %新乡: 0.1 %新乡: 0.1 %旧金山: 0.0 %旧金山: 0.0 %昆明: 0.4 %昆明: 0.4 %晋城: 0.0 %晋城: 0.0 %朔州: 0.0 %朔州: 0.0 %杭州: 1.5 %杭州: 1.5 %格兰特县: 0.2 %格兰特县: 0.2 %桂林: 0.1 %桂林: 0.1 %榆林: 0.0 %榆林: 0.0 %武汉: 0.6 %武汉: 0.6 %沈阳: 0.1 %沈阳: 0.1 %沙田: 0.0 %沙田: 0.0 %洛杉矶: 0.1 %洛杉矶: 0.1 %济南: 0.1 %济南: 0.1 %海得拉巴: 0.1 %海得拉巴: 0.1 %淮南: 0.0 %淮南: 0.0 %淮安: 0.1 %淮安: 0.1 %深圳: 0.8 %深圳: 0.8 %深圳市: 0.0 %深圳市: 0.0 %温州: 0.1 %温州: 0.1 %渭南: 0.1 %渭南: 0.1 %湖州: 0.1 %湖州: 0.1 %湘潭: 0.1 %湘潭: 0.1 %漯河: 0.1 %漯河: 0.1 %漳州: 0.0 %漳州: 0.0 %濮阳: 0.0 %濮阳: 0.0 %烟台: 0.1 %烟台: 0.1 %焦作: 0.0 %焦作: 0.0 %珠海: 0.1 %珠海: 0.1 %白银: 0.0 %白银: 0.0 %石家庄: 0.2 %石家庄: 0.2 %福州: 0.2 %福州: 0.2 %科珀斯克里斯蒂: 0.0 %科珀斯克里斯蒂: 0.0 %秦皇岛: 0.1 %秦皇岛: 0.1 %纽约: 0.1 %纽约: 0.1 %绍兴: 0.2 %绍兴: 0.2 %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 %美国加利福尼亚洛杉矶: 0.1 %美国加利福尼亚洛杉矶: 0.1 %芒廷维尤: 19.4 %芒廷维尤: 19.4 %芝加哥: 0.2 %芝加哥: 0.2 %苏州: 0.2 %苏州: 0.2 %菏泽: 0.1 %菏泽: 0.1 %蚌埠: 0.0 %蚌埠: 0.0 %衡水: 0.1 %衡水: 0.1 %衡阳: 0.0 %衡阳: 0.0 %衢州: 0.2 %衢州: 0.2 %西宁: 27.7 %西宁: 27.7 %西安: 1.2 %西安: 1.2 %诺沃克: 0.0 %诺沃克: 0.0 %贵港: 0.2 %贵港: 0.2 %费利蒙: 0.0 %费利蒙: 0.0 %运城: 0.1 %运城: 0.1 %邯郸: 0.0 %邯郸: 0.0 %邵阳: 0.0 %邵阳: 0.0 %郑州: 1.3 %郑州: 1.3 %重庆: 0.2 %重庆: 0.2 %长春: 0.1 %长春: 0.1 %长沙: 1.0 %长沙: 1.0 %阳泉: 0.0 %阳泉: 0.0 %青岛: 0.4 %青岛: 0.4 %首尔: 0.4 %首尔: 0.4 %黄冈: 0.1 %黄冈: 0.1 %黄山: 0.0 %黄山: 0.0 %黄石: 0.1 %黄石: 0.1 %其他其他BadenCentral DistrictChinaHanoiHungaryIndiaIran (ISLAMIC Republic Of)Kao-sungKennedy TownKorea Republic ofMexicoSeattleSpainThailandUnited KingdomUnited StatesWixom[]上海东莞中卫临沂乌兰察布佛山保定兰州内江加利福尼亚包头北京北海十堰南京南宁南昌厦门古吉拉特台北台州合肥呼和浩特哈尔滨商丘嘉兴大连天津威海安科纳宜宾宜春宣城宿迁巴音郭楞常州常德平凉平顶山广州库比蒂诺廊坊延安开封张家口张家口市徐州德里久尔意大利成都扬州新乡旧金山昆明晋城朔州杭州格兰特县桂林榆林武汉沈阳沙田洛杉矶济南海得拉巴淮南淮安深圳深圳市温州渭南湖州湘潭漯河漳州濮阳烟台焦作珠海白银石家庄福州科珀斯克里斯蒂秦皇岛纽约绍兴绵阳美国伊利诺斯芝加哥美国加利福尼亚洛杉矶芒廷维尤芝加哥苏州菏泽蚌埠衡水衡阳衢州西宁西安诺沃克贵港费利蒙运城邯郸邵阳郑州重庆长春长沙阳泉青岛首尔黄冈黄山黄石

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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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