3D TOTAL VARIATION HYPERSPECTRAL COMPRESSIVE SENSING USING UNMIXING

被引：4

作者：

Zhang, Lei ^{[1
]}

Zhang, Yanning ^{[1
]}

Wei, Wei ^{[1
]}

Li, Fei ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China

来源：

2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2014年

关键词：

3D Total Variation Prior; Hyperspectral Compressive Sensing; Hyperspectral Linear Unmixing;

D O I：

10.1109/IGARSS.2014.6947098

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

To reduce the huge resource consumption in the hyperspectral imaging and transmission, this paper proposes a high-performance compression method. Specially, a novel 3D total variation prior is imposed on abundance fractions of end-members. In this method, compressed data is obtained by a random observation matrix in a compressive sensing way. Based on the hyperspectral linear mixed model and known endmembers, abundance fractions are estimated by an augmented Lagrangian method with the devised prior and then the original data is reconstructed. Extensive experimental results demonstrate the superiority of the proposed method to several state-of-art methods.

引用

页数：4