Low-rank representation for 3D hyperspectral images analysis from map perspective

doi:10.1016/j.sigpro.2014.06.018

OPT OpenIR > 光谱成像技术研究室

	Low-rank representation for 3D hyperspectral images analysis from map perspective
	Yuan, Yuan; Fu, Min; Lu, Xiaoqiang
作者部门	光学影像学习与分析中心
	2015-07-01
发表期刊	SIGNAL PROCESSING
ISSN	0165-1684
卷号	112 页码:27-33
摘要	Hyperspectral images naturally stand as 3D data, which carry semantic information in remote sending applications. To well utilize 3D hyperspectral images, signal processing and learning techniques have been widely exploited, and the basis is to divide a given hyperspectral data into a set of semantic classes for analysis, i.e., segmentation. To segment given hyperspectral data is an important and challenging research theme. Recently, to reduce the amount of human labor required to label samples in hyperspectral image segmentation, many approaches have been proposed and achieved good performance with a few labeled samples. However, most of them fail to exploit the high spectral correlation in distinct bands and utilize the spatial information of hyperspectral data. In order to overcome these drawbacks, a novel framework jointing the maximum a posteriori (MAP) model and low-rank representation (LRR) is proposed. In this paper, low-rank representation, conducted as a latent variables, can exploit the high spectral correlation in distinct bands and obtain a more compact and discriminative representation. On the other hand, a novel MAP framework is driven by using low-rank representation coefficient as latent variables, which will improve the probability that the closer pixels can be divided into the same class. The experiment results and quantitative analysis demonstrate that the proposed approach is effective and can obtain high segmentation accuracy compared with state-of-the-art approaches. (C) 2014 Elsevier B.V. All rights reserved.
文章类型	Article
关键词	3d Hyperspectral Data Semantic Analysis Segmentation Markov Random Field (Mrf) Low-rank Representation (Lrr) Maximum a Posteriori (Map) Remote Sensing
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.sigpro.2014.06.018
收录类别	SCI ; EI
关键词[WOS]	ENERGY MINIMIZATION ; CLASSIFICATION ; NMF
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000351976400004
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/22410
专题	光谱成像技术研究室
作者单位	Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Yuan, Yuan,Fu, Min,Lu, Xiaoqiang. Low-rank representation for 3D hyperspectral images analysis from map perspective[J]. SIGNAL PROCESSING,2015,112:27-33.
APA	Yuan, Yuan,Fu, Min,&Lu, Xiaoqiang.(2015).Low-rank representation for 3D hyperspectral images analysis from map perspective.SIGNAL PROCESSING,112,27-33.
MLA	Yuan, Yuan,et al."Low-rank representation for 3D hyperspectral images analysis from map perspective".SIGNAL PROCESSING 112(2015):27-33.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Low-rank representat（1225KB）	期刊论文	出版稿	限制开放	CC BY	请求全文