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Pixel type classification based reversible data hiding for hyperspectral images

文献类型：期刊论文

作者Fan, Guojun3; Pan, Zhibin3; Zhou, Quan1; Dong, Jing2; Zhang, Xiaoran3
刊名KNOWLEDGE-BASED SYSTEMS
出版日期2022-10-27
卷号254页码:11
关键词Hyperspectral images Pixel classifying Adaptive prediction Three -components complexity Reversible data hiding
ISSN号0950-7051
DOI10.1016/j.knosys.2022.109606
通讯作者Pan, Zhibin(zbpan@mail.xjtu.edu.cn)
英文摘要The digital files are becoming larger and larger with the development of computer hardware and computing power, and the fast processing for large files, e.g., hyperspectral images, is becoming feasible for not only companies but also individuals. However, the acquirement of hyperspectral images still costs a lot. Therefore, security issues like copyright ownership of hyperspectral images need to be taken seriously. Reversible data hiding (RDH) is a technology that can embed watermarking information into multimedia cover to protect copyright. However, the natural-images-based RDH methods cannot exploit the large amount of inter-band redundancy contained by hyperspectral images, which leads to a low efficiency for copyright protection. In this paper, a novel RDH method specially designed for hyperspectral images is proposed. We use the value information from the pixel of an adjacent band to classify each pixel into one of the five types when predicting it, and an adaptive predictor is matched for the pixels of each type to achieve a high prediction accuracy. Finally, a complexity calculation method containing three components is put forward to further improve the embedding performance. Experiments show that the proposed method outperforms the existing RDH method for hyperspectral images and other state-of-the-art RDH methods for natural images. (C) 2022 Elsevier B.V. All rights reserved.
WOS关键词LOSSLESS DATA ; PREDICTION ; EXPANSION ; WATERMARKING
资助项目National Natural Science Foundation of China[U1903213] ; Open Project of the National Laboratory of Pattern Recognition[202100033] ; Open Foundation of Henan Key Laboraty of Cyberspace Situation Awareness[HNTS2022015] ; Zhejiang Provincial Commonweal Project[LGF21F030002]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000861031200010
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Open Project of the National Laboratory of Pattern Recognition ; Open Foundation of Henan Key Laboraty of Cyberspace Situation Awareness ; Zhejiang Provincial Commonweal Project
源URL[http://ir.ia.ac.cn/handle/173211/50419]  
专题自动化研究所_智能感知与计算研究中心
通讯作者Pan, Zhibin
作者单位1.CAST, Natl Key Lab Sci & Technol Space Microwave, Xian 710100, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Xi An Jiao Tong Univ, Fac Elect & Informat Engn, Xian 710049, Peoples R China
推荐引用方式
GB/T 7714		 Fan, Guojun,Pan, Zhibin,Zhou, Quan,et al. Pixel type classification based reversible data hiding for hyperspectral images[J]. KNOWLEDGE-BASED SYSTEMS,2022,254:11.
APA Fan, Guojun,Pan, Zhibin,Zhou, Quan,Dong, Jing,&Zhang, Xiaoran.(2022).Pixel type classification based reversible data hiding for hyperspectral images.KNOWLEDGE-BASED SYSTEMS,254,11.
MLA Fan, Guojun,et al."Pixel type classification based reversible data hiding for hyperspectral images".KNOWLEDGE-BASED SYSTEMS 254(2022):11.

入库方式： OAI收割

来源：自动化研究所

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