| 基于高分一号遥感影像的绿地信息提取 |
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| 引用本文： | 李巍,丁晨旸,李萍.基于高分一号遥感影像的绿地信息提取[J].安徽农业科学,2017,45(14). |
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| 作者姓名： | 李巍 丁晨旸 李萍 |
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| 作者单位： | 1. 黑龙江科技大学矿业工程学院,黑龙江哈尔滨,150022;2. 东北林业大学园林学院,黑龙江哈尔滨 150001;东北农业大学园艺园林学院,黑龙江哈尔滨 150040;3. 山东省东营市园林局,山东东营,257100 |
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| 基金项目： | 东营园林局横向项目，黑龙江科技大学教学研究项目 |
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| 摘 要： | 对高分一号卫星影像进行大气校正、几何校正、裁剪等,利用Libsvm 4.0在Matlab平台里编程进行交叉验证网格法寻优,最终获得支持向量机分类的最佳惩罚系数为45,不敏感系数为0.31。改进支持向量机分类器绿地分类精度为94.6%,该提取精度能满足高分辨率遥感影像在城市绿地动态监测。
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| 关 键 词： | 遥感 高分一号影像 城市绿地 支持向量机分类器 |
Extraction of Urban Green Space Information Based on GF-1 Remote Sensing Images |
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| LI Wei,DING Chen-yang,LI Ping.Extraction of Urban Green Space Information Based on GF-1 Remote Sensing Images[J].Journal of Anhui Agricultural Sciences,2017,45(14). |
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| Authors: | LI Wei DING Chen-yang LI Ping |
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| Abstract: | The atmospheric correction, geometric correction, cutting were conducted on GF-1 satellite images.The cross validation grid optimization was made in Matalb platform by Libsvm 4.0.The best penalty coefficient of support vector machine classifier was 45, and sensitivity coefficient was 0.31.The results showed that the classification accuracy was 94.6%, and the extraction accuracy can meet the high resolution remote sensing images in dynamic monitoring of urban green space. |
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| Keywords: | Remote sensing GF-1 image Urban green space Support vector classifier |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
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