Zero-Watermark Method Based on Multichannel PCNN and Blockchain for Remote Sensing Image Transaction Certificate and Copyright Protection

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 handcrafted 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 a multichannel pulse-coupled neural network (PCNN) and blockchain for remote sensing image transaction certificate and copyright protection is proposed. The method designs a multichannel PCNN model to process each band of remote sensing images in parallel and uses multiscale morphological gradient (MSMG) and differential box counting (DBC) to optimize and motivate the model to enhance the robustness of zero watermarks. The distinguishing performance of zero watermarks for copyright information is enhanced according to the dynamic features and spatiotemporal characteristics of the PCNN time series. In addition, given that the characteristics of remote sensing image data volume are huge and involve private and sensitive information, a zero-watermark registration and extraction framework integrating Hyperledger Fabric and Interplanetary File System (IPFS) is constructed, aiming to achieve the purpose of transaction verification and copyright protection.