Zero Watermarking Algorithm for Hyperspectral Remote Sensing Images Considering Spectral and Spatial Features
Most existing zero-watermarking algorithms for remote sensing images are designed for panchromatic or multispectral data. When applied to hyperspectral data, these methods fail to fully utilize the unique characteristics of hyperspectral images, resulting in poor robustness. This study proposes a no...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10956168/ |
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| author | Bingbing Yang Haowen Yan Liming Zhang Qingbo Yan Zhaoyang Hou Xiaolong Wang Xinyu Xu |
| author_facet | Bingbing Yang Haowen Yan Liming Zhang Qingbo Yan Zhaoyang Hou Xiaolong Wang Xinyu Xu |
| author_sort | Bingbing Yang |
| collection | DOAJ |
| description | Most existing zero-watermarking algorithms for remote sensing images are designed for panchromatic or multispectral data. When applied to hyperspectral data, these methods fail to fully utilize the unique characteristics of hyperspectral images, resulting in poor robustness. This study proposes a novel zero-watermarking method specifically tailored for hyperspectral remote sensing images, leveraging both spectral and spatial features. First, principal component analysis is applied to reduce the dimensionality of the hyperspectral image, and the leading principal component is used for K-means clustering. The frequency of each category is calculated based on the clustering results, and the category frequency vector is used as the spectral feature vector. Simultaneously, Zernike moments are employed to extract spatial features, generating the spatial feature vector. These spectral and spatial feature vectors are combined to form the carrier image feature vector. Next, the watermark image is scrambled using Zigzag scanning and binarized. The mixed linear-nonlinear coupled map lattice chaotic system generates a random sequence, which is XORed with the binarized watermark sequence to produce the final binary watermark sequence. Finally, the carrier image feature vector and the binary watermark sequence are XORed to generate the zero watermark. The experimental results show that under the common attacks such as geometric attack, noise attack, filtering attack, and combination attack, the normalized correlation (NC) values of the proposed algorithm are high, all above 0.9, the values of BER are low, and the NC values of fusing spectral and spatial features are higher than that of extracting only spectral or spatial features. These results show that the proposed method effectively exploits the data characteristics of hyperspectral remote sensing images, is robust to various attacks, does not cause loss of accuracy, and is suitable for copyright protection of hyperspectral remote sensing image data. |
| format | Article |
| id | doaj-art-c01d3e9535664b9a80387a3d814f97ab |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-c01d3e9535664b9a80387a3d814f97ab2025-08-20T03:13:47ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118108821089510.1109/JSTARS.2025.355891910956168Zero Watermarking Algorithm for Hyperspectral Remote Sensing Images Considering Spectral and Spatial FeaturesBingbing Yang0https://orcid.org/0009-0000-5284-748XHaowen Yan1https://orcid.org/0000-0002-2792-3425Liming Zhang2https://orcid.org/0000-0001-7904-7044Qingbo Yan3https://orcid.org/0009-0001-9560-5285Zhaoyang Hou4https://orcid.org/0000-0001-9894-4795Xiaolong Wang5https://orcid.org/0000-0002-6518-423XXinyu Xu6Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou, ChinaNational Glacial Permafrost Desert Science Data Centre, Northwest Institute of Ecology and Environmental Resources, Chinese Academy of Sciences, Lanzhou, ChinaMost existing zero-watermarking algorithms for remote sensing images are designed for panchromatic or multispectral data. When applied to hyperspectral data, these methods fail to fully utilize the unique characteristics of hyperspectral images, resulting in poor robustness. This study proposes a novel zero-watermarking method specifically tailored for hyperspectral remote sensing images, leveraging both spectral and spatial features. First, principal component analysis is applied to reduce the dimensionality of the hyperspectral image, and the leading principal component is used for K-means clustering. The frequency of each category is calculated based on the clustering results, and the category frequency vector is used as the spectral feature vector. Simultaneously, Zernike moments are employed to extract spatial features, generating the spatial feature vector. These spectral and spatial feature vectors are combined to form the carrier image feature vector. Next, the watermark image is scrambled using Zigzag scanning and binarized. The mixed linear-nonlinear coupled map lattice chaotic system generates a random sequence, which is XORed with the binarized watermark sequence to produce the final binary watermark sequence. Finally, the carrier image feature vector and the binary watermark sequence are XORed to generate the zero watermark. The experimental results show that under the common attacks such as geometric attack, noise attack, filtering attack, and combination attack, the normalized correlation (NC) values of the proposed algorithm are high, all above 0.9, the values of BER are low, and the NC values of fusing spectral and spatial features are higher than that of extracting only spectral or spatial features. These results show that the proposed method effectively exploits the data characteristics of hyperspectral remote sensing images, is robust to various attacks, does not cause loss of accuracy, and is suitable for copyright protection of hyperspectral remote sensing image data.https://ieeexplore.ieee.org/document/10956168/K-meansspatial featuresspectral featureszero watermarking |
| spellingShingle | Bingbing Yang Haowen Yan Liming Zhang Qingbo Yan Zhaoyang Hou Xiaolong Wang Xinyu Xu Zero Watermarking Algorithm for Hyperspectral Remote Sensing Images Considering Spectral and Spatial Features IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing K-means spatial features spectral features zero watermarking |
| title | Zero Watermarking Algorithm for Hyperspectral Remote Sensing Images Considering Spectral and Spatial Features |
| title_full | Zero Watermarking Algorithm for Hyperspectral Remote Sensing Images Considering Spectral and Spatial Features |
| title_fullStr | Zero Watermarking Algorithm for Hyperspectral Remote Sensing Images Considering Spectral and Spatial Features |
| title_full_unstemmed | Zero Watermarking Algorithm for Hyperspectral Remote Sensing Images Considering Spectral and Spatial Features |
| title_short | Zero Watermarking Algorithm for Hyperspectral Remote Sensing Images Considering Spectral and Spatial Features |
| title_sort | zero watermarking algorithm for hyperspectral remote sensing images considering spectral and spatial features |
| topic | K-means spatial features spectral features zero watermarking |
| url | https://ieeexplore.ieee.org/document/10956168/ |
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