QQ客服 官方微博 反馈留言

	Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging (EI收录)
	Zabalza, Jaime[1]; Ren, Jinchang[1]; Zheng, Jiangbin[2]; Zhao, Huimin[3]; Qing, Chunmei[4]; Yang, Zhijing[5]; Du, Peijun[6]; Marshall, Stephen[1]
刊名	Neurocomputing
	2016
卷号	185页码:1-10
关键词	Abstracting Classification (of information) Extraction Feature extraction Learning systems Remote sensing Spectroscopy
URL标识	查看原文
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2192973
专题	华南理工大学
作者单位	1.[1] Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, United Kingdom 2.[2] School of Microelectronics and Software, Northwestern Polytechnical University, Xi'an, China 3.[3] School of Electronic and Information, Guangdong Technic Normal University, Guangzhou, China 4.[4] School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China 5.[5] School of Information Engineering, Guangdong University of Technology, Guangzhou, China 6.[6] Dept. of Geographical Information, Nanjing University, Nanjing, China
推荐引用方式 GB/T 7714	Zabalza, Jaime[1],Ren, Jinchang[1],Zheng, Jiangbin[2],等. Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging (EI收录)[J]. Neurocomputing,2016,185:1-10.
APA	Zabalza, Jaime[1].,Ren, Jinchang[1].,Zheng, Jiangbin[2].,Zhao, Huimin[3].,Qing, Chunmei[4].,...&Marshall, Stephen[1].(2016).Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging (EI收录).Neurocomputing,185,1-10.
MLA	Zabalza, Jaime[1],et al."Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging (EI收录)".Neurocomputing 185(2016):1-10.

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。