Classification of Hyperspectral Remote Sensing Image Based onGenetic Algorithm and SVM

著录项

• 来源
  《Remote sensing and modeling of ecosystems for sustainability VII》|2010年|p.78090A.1-78090A.9|共9页
• 会议地点 San Diego CA(US)
• 作者

  Mandi Zhou; Jiong Shu; Zhigang Chen;

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• 作者单位

  Key Lab. of Geographic Information Science, East China Normal University, 3663 NorthrnZhongshan Road, Shanghai, China;

  Key Lab. of Geographic Information Science, East China Normal University, 3663 NorthrnZhongshan Road, Shanghai, China;

  Key Lab. of Geographic Information Science, East China Normal University, 3663 NorthrnZhongshan Road, Shanghai, China;

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• 会议组织
• 原文格式 PDF
• 正文语种 eng
• 中图分类 遥感技术;
• 关键词

  Hyperspectral remote sensing data has been widely used in Terrain Classification for its high resolution. The classification of urban vegetation, identified as an indispensable and essential part of urban development system, is now facing a major challenge as different complex land-cover classes having similar spectral signatures. For a better accuracy in classification of urban vegetation, a classifier model was designed in this paper based on genetic algorithm (GA) and support vector machine (;

  机译：高光谱遥感数据因其高分辨率而被广泛用于地形分类中。城市植被的分类已被确定为城市发展系统必不可少的重要组成部分，目前正面临一项重大挑战，因为不同的复杂土地覆盖类别具有相似的光谱特征。为了提高城市植被分类的准确性，本文基于遗传算法（GA）和支持向量机（;