Pixel type classification based reversible data hiding for hyperspectral images | |
Fan, Guojun2; Pan, Zhibin2; Zhou, Quan1; Dong, Jing3; Zhang, Xiaoran2 | |
刊名 | KNOWLEDGE-BASED SYSTEMS |
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 |
DOI | 10.1016/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. |
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关键词 | LOSSLESS DATA ; PREDICTION ; EXPANSION ; WATERMARKING |
WOS研究方向 | Computer Science |
语种 | 英语 |
出版者 | ELSEVIER |
WOS记录号 | WOS:000861031200010 |
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 |
内容类型 | 期刊论文 |
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.Xi An Jiao Tong Univ, Fac Elect & Informat Engn, Xian 710049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, 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. |
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