An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery | |
Huang, Xin; Zhang, Liangpei | |
刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
![]() |
2013 | |
卷号 | 51期号:1 |
关键词 | Classification feature extraction high resolution morphological multifeature object-based semantic support vector machines (SVMs) WorldView-2 |
ISSN号 | 0196-2892 |
DOI | 10.1109/TGRS.2012.2202912 |
URL标识 | 查看原文 |
收录类别 | SCIE ; EI ; ESI高被引论文 |
语种 | 英语 |
内容类型 | 期刊论文 |
URI标识 | http://www.corc.org.cn/handle/1471x/4119349 |
专题 | 武汉大学 |
推荐引用方式 GB/T 7714 | Huang, Xin,Zhang, Liangpei. An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2013,51(1). |
APA | Huang, Xin,&Zhang, Liangpei.(2013).An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,51(1). |
MLA | Huang, Xin,et al."An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 51.1(2013). |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论