期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 8, 期 6, 页码 2351-2360出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2014.2359965
关键词
Composite kernel (CK); extreme learning machine (ELM); hyperspectral image (HSI) classification
类别
资金
- Macau Science and Technology Development Fund [FDCT/017/2012/A1]
- Research Committee at the University of Macau [MYRG2014-00003-FST, MRG017/ZYC/2014/FST, MYRG113(Y1-L3)-FST12-ZYC, MRG001/ZYC/2013/FST]
- National Natural Science Foundation of China [11371007]
Due to its simple, fast, and good generalization ability, extreme learning machine (ELM) has recently drawn increasing attention in the pattern recognition and machine learning fields. To investigate the performance of ELM on the hyperspectral images (HSIs), this paper proposes two spatial-spectral composite kernel (CK) ELM classification methods. In the proposed CK framework, the single spatial or spectral kernel consists of activation-function-based kernel and general Gaussian kernel, respectively. The proposed methods inherit the advantages of ELM and have an analytic solution to directly implement the multiclass classification. Experimental results on three benchmark hyperspectral datasets demonstrate that the proposed ELM with CK methods outperform the general ELM, SVM, and SVM with CK methods.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据