文献类型：期刊文献

英文题名：Feature selection and multiple kernel boosting framework based on PSO with mutation mechanism for hyperspectral classification

作者：Qi, Chengming[1,2];Zhou, Zhangbing[1,3];Sun, Yunchuan[4];Song, Houbing[5];Hu, Lishuan[1,2];Wang, Qun[1]

第一作者：亓呈明;Qi, Chengming

通讯作者：Zhou, ZB[1]

机构：[1]China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China;[2]Beijing Union Univ, Coll Automat, Beijing 100101, Peoples R China;[3]TELECOM SudParis, Dept Comp Sci, F-91001 Evry, France;[4]Beijing Normal Univ, Sch Business, Beijing 100875, Peoples R China;[5]West Virginia Univ, Secur & Optimizat Networked Globe Lab, Montgomery, WV 25136 USA

第一机构：China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China

通讯机构：[1]corresponding author), China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China.

年份：2017

卷号：220

起止页码：181-190

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20164803061077);Scopus(收录号：2-s2.0-84997017371);WOS:【SCI-EXPANDED(收录号:WOS:000390505200020)】；

基金：Houbing Song (M12-SM14) received the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, VA, in August 2012. In August 2012, he joined the Department of Electrical and Computer Engineering, West Virginia University, Montgomery, WV, where he is currently an Assistant Professor and the founding director of the Security and Optimization for Networked Globe Laboratory (SONG La. His research interests lie in the areas of cyber-physical systems, internet of things, cloud computing, big data, connected vehicle, wireless communications and networking, and optical communications and networking. Dr. Songs research has been supported by the West Virginia Higher Education Policy Commission. Dr. Song is a senior member of IEEE and a member of ACM. Dr. Song is an associate editor for several international journals, including IEEE Access, KSII Transactions on Internet and Information Systems, and SpringerPlus and a guest editor of several special issues. Dr. Song was the general chair of 4 international workshops, including the first IEEE International Workshop on Security and Privacy for Internet of Things and Cyber-Physical Systems (IOT/CPS-Security), held in London, UK, the first/second/third IEEE ICCC International Workshop on Internet of Things (IOT 2013/2014/2015), held in Xian/Shanghai/Shenzhen, China, and the first IEEE International Workshop on Big Data Analytics for Smart and Connected Health, to be held in Washington D.C., USA. Dr. Song also served as the technical program committee chair of the fourth IEEE International Workshop on Cloud Computing Systems, Networks, and Applications (CCSNA), held in San Diego, USA. Dr. Song has served on the technical program committee for numerous international conferences, including ICC, GLOBECOM, INFOCOM, WCNC, and so on. Dr. Song has published more than 80 academic papers in peer-reviewed international journals and conferences.

语种：英文

外文关键词：Ensemble learning; Feature selection; Hyperspectral remote sensing image; Multiple kernel boosting

摘要：Hyperspectral remote sensing sensors can capture hundreds of contiguous spectral images and provide plenty of valuable information. Feature selection and classification play a key role in the field of HyperSpectral Image (HSI) analysis. This paper addresses the problem of HSI classification from the following three aspects. First, we present a novel criterion by standard deviation, Kullback-Leibler distance, and correlation coefficient for feature selection. Second, we optimize the SVM classifier design by searching for the most appropriate value of the parameters using particle swarm optimization (PSO) with mutation mechanism. Finally, we propose an ensemble learning framework, which applies the boosting technique to learn multiple kernel classifiers for classification problems. Experiments are conducted on benchmark HSI classification data sets. The evaluation results show that the proposed approach can achieve better accuracy and efficiency than state-of-the-art methods. (C) 2016 Elsevier B.V. All rights reserved.