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

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页码：1815 / 1822

页数：8

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