Hyperspectral unmixing based on the sparisity of spectrum's gabor transform coefficients

CASIA OpenIR > 智能制造技术与系统研究中心 > 多维数据分析（彭思龙）-技术团队

	Hyperspectral unmixing based on the sparisity of spectrum's gabor transform coefficients
	Wang, Mengxin; Peng, Silong; Mengxin Wang
	2011
会议名称	2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011 - Proceedings
会议录名称	International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE)
页码	389-393
会议日期	2011
摘要	Nonnegative matrix factorization (NMF), which has shown success in blind source separation, has also been applied to hyperspectral data unmixing. Many constraints have been introduced to render better estimates. However, most algorithms in this respect have not explored the inner characteristics of the spectrum and the formation of the spectrum itself. In this paper a novel sparisity constrained nonnegative matrix factorization (SCNMF) is proposed, which investigates the sparisty characteristic of the spectrum in its gabor transform domain. Results obtained with synthetic and real data are used to illustrate the effectiveness of the proposed method.
关键词	Nonnegative Matrix Factorization Gabor Transform domain Sparisity Constrained Gaussian-shape.
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/5176
专题	智能制造技术与系统研究中心_多维数据分析（彭思龙）-技术团队
通讯作者	Mengxin Wang
推荐引用方式 GB/T 7714	Wang, Mengxin,Peng, Silong,Mengxin Wang. Hyperspectral unmixing based on the sparisity of spectrum's gabor transform coefficients[C],2011:389-393.