GAS PLUME DETECTION IN HYPERSPECTRAL VIDEO SEQUENCE USING LOW RANK REPRESENTATION

被引：0

作者：

Xu, Yang ^{[1
]}

Wu, Zebin ^{[1
]}

Wei, Zhihui ^{[1
]}

Dalla Mura, Mauro ^{[2
]}

Chanussot, Jocelyn ^{[2
]}

Bertozzi, Andrea ^{[3
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China

[2] Grenoble Inst Technol, GIPSA Lab, Grenoble, France

[3] Univ Calif Los Angeles, Dept Math, Los Angeles, CA 90024 USA

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2016年

基金：

美国国家科学基金会;

关键词：

gas plume detection; hyperspectral video sequence; low rank representation; total variation;

D O I：

暂无

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：

Thanks to the fast development of sensors, it is now possible to acquire sequences of hyperspectral images. Those hyperspectral video sequences (HVS) are particularly suited for the detection and tracking of chemical gas plumes. In this paper, we present a novel gas plume detection method. It is based on the decomposition of the sequence into a low-rank and a sparse term, corresponding to the background and the plume, respectively, and incorporating temporal consistency. To introduce spatial continuity, a post processing is added using the Total Variation (TV) regularized model. Experimental results on real hyperspectral video sequences validate the effectiveness of the proposed method.

引用

页码：2221 / 2225

页数：5

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