Compression of hyperspectral images based on Tucker decomposition and CP decomposition

被引：2
|
作者：
Yang, Lei [1 ,2 ,3 ]
Zhou, Jinsong [1 ,2 ,3 ]
Jing, Juanjuan [1 ,2 ,3 ]
Wei, Lidong [1 ,3 ]
Li, Yacan [1 ,3 ]
He, Xiaoying [1 ,3 ]
Feng, Lei [1 ,3 ]
Nie, Boyang [1 ,3 ]
机构：
[1] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
[2] Univ Chinese Acad Sci, Sch Optoelect, Beijing 100049, Peoples R China
[3] Chinese Acad Sci, Key Lab Computat Opt Imaging Technol, Beijing 100094, Peoples R China
来源：
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION | 2022年 / 39卷 / 10期
基金：
中国国家自然科学基金;
关键词：
IMAGING SPECTROMETER; DESIGN;
D O I：
10.1364/JOSAA.468167
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
Hyperspectral imagers are developing towards high resolution, high detection sensitivity, broad spectra, and wide coverage, which means that hyperspectral data are getting more and more substantial. This brings a great challenge to data storage and real-time transmission of hyperspectral data. A compression method based on Tucker decomposition and CANDECOMP/PARAFAC decomposition (TD-CP) is proposed. The hyperspectral data are treated as a third-order tensor. First, TD is performed on the hyperspectral data to obtain a core tensor and three factor matrices, and then CP decomposition is performed on the core tensor. Compared with principal component analysis (PCA)+JPEG2000, TD, and CP, TD-CP can retain spatial information and spectral information better at the same time, and running time is shorter. (c) 2022 Optica Publishing Group
引用
收藏
页码：1815 / 1822
页数：8
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