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SVM用于LiDAR数据的地物分类 被引量：9

Land Cover Classification from LiDAR Data Based on SVM
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摘要 LiDAR具有探测地表垂向结构的能力,目前还无法被其他遥感源所取代。本文提出通过变换点云提取LiDAR纹理特征,利用支持向量机(SVM)进行训练和分类,并与基于神经网络的分类方法进行比较。试验结果表明,SVM能在训练精度和推广能力之间取得折中,可有效地避免LiDAR地物分类证据不完备引起的过拟合问题,适合于LiDAR地物分类。
作者 乔纪纲 陈明辉 艾彬 张亦汉
机构地区 广东商学院资源与环境学院 东莞市地理信息与规划编制研究中心 中山大学海洋学院
出处 《测绘通报》 CSCD 北大核心 2013年第7期35-38,42,共5页 Bulletin of Surveying and Mapping
基金 广东省自然科学基金(S2011040003226) 广州市科技计划(7421252729755)
关键词 LIDAR 高度纹理 SVM
分类号 P237 [天文地球—摄影测量与遥感]
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参考文献11

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二级参考文献18

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共引文献36

同被引文献57

引证文献9

二级引证文献32

测绘通报
2013年 第7期

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