Pixel type classification based reversible data hiding for hyperspectral images

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.