The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Zero-Watermark Method Based on Multichannel PCNN and Blockchain for Remote Sensing Image Transaction Certificate and Copyright Protection
Abstract
The current remote sensing image data sharing frameworks confront dual challenges of trusted transaction and copyright protection. On the one hand, existing zero-watermark schemes are constrained by the hand-crafted feature extraction paradigms, which struggle to capture the intricate high-dimensional spectral-spatial joint feature representations inherent in remote sensing imagery. On the other hand, most data trading protocols fail to harmonize fairness in transactions with robust copyright protection. To this end, a zero-watermark method based on multi-channel PCNN and blockchain for remote sensing image transaction certificate and copyright protection is proposed. The method designs a multi-channel PCNN model to process each band of remote sensing images in parallel, and uses multi-scale morphological gradient and differential box counting to optimize and motivate the model to enhance the robustness of zero-watermark. The distinguishing performance of zero-watermarks for copyright information is enhanced according to the dynamic features and spatio-temporal characteristics of PCNN time series. In addition, given the characteristics of remote sensing image data volume is huge and involves private and sensitive information, a zero-watermark registration and extraction framework integrating Hyperledger Fabric and Inter-Planetary File System is constructed, aiming to achieve the purpose of transaction verification and copyright protection.
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In the citing paper
“Reliance on trusted third parties is eliminated with these frameworks, yet there has not been equal emphasis placed on how well the perceptual authentication mechanism functions.”
From DBpHash: a blockchain-based dual-band perceptual hashing framework for copyright protection of purely chromatic background images (2026) · cites this paper as [7]
In the citing paper
“The blockchain primarily provides a durable registry, while the distortion tolerance levels and decision conservativeness of the similarity model continue to be secondary in consideration.7,8.”
From DBpHash: a blockchain-based dual-band perceptual hashing framework for copyright protection of purely chromatic background images (2026) · cites this paper as [7]
Citations
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A survey on blockchain for big data: Approaches, opportunities, and future directions
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A Survey on Ethereum Systems Security
Chen, H., Pendleton, M., Njilla, L., & Xu, S. (2020) · ACM Computing Surveys · doi:10.1145/3391195 · Cited by 484
A comprehensive survey on robust image watermarking
Wan, W., Wang, J., Zhang, Y., Li, J., Yu, H. L., & Sun, J. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.02.083 · Cited by 279
Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure
Zhang, Y., Bai, X., & Wang, T. (2016) · Information Fusion · doi:10.1016/j.inffus.2016.09.006 · Cited by 276
Uddin, M. (2021) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2021.120235 · Cited by 253
Differential box counting methods for estimating fractal dimension of gray-scale images: A survey
Panigrahy, C., Seal, A., Mahato, N. K., & Bhattacharjee, D. (2019) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2019.06.007 · Cited by 146
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Cited by
Sorted by citations
Nainar, M., Sankaranarayanan, S., & Veeramani, K. (2026) · Scientific Reports · doi:10.1038/s41598-026-54102-1
Precision Controllable Reversible Watermarking Algorithm for Oblique Photography 3D Models
Qu, R., Zhang, L., Hou, Z., & Zhang, M. (2025) · Sensors · doi:10.3390/s26010243
Self-citationOptically Authenticated Hyperspectral Imaging: Detection and Restoration of Manipulated Data
Gómez, P. I. F., Jácome, R., García, H., Esnaola, I., & Arguello, H. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3697764
Qu, R., Yan, H., Zhang, L., Hou, Z., Yang, F., & Chen, H. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3718872
Self-citationLeng, X., Wang, X., Zhang, Z., & Zeng, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3720399
Metadata
- Authors
- Hou, Z., Yan, H., Zhang, L., Ren, N., Ma, R., & Qu, R.
- Affiliations
- Lanzhou Jiaotong University (CN)
- Nanjing Normal University (CN)
- Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, China
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2025
- Volume
- 63
- Pages
- 1-15
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W4410950133
- Cited by
- 3
- References
- 42
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
- Funders
- National Natural Science Foundation of China (42430108, 42271430)
- Gansu Province Outstanding Graduate Innovation Star Program (2025CXZX-628)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Keywords
- zero-watermarking, blockchain, Hyperledger Fabric, IPFS, spectral-spatial features, differential box counting, watermark robustness
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