Remote sensing data has found widespread applications in areas such as military monitoring, environmental management, and urban planning. However, the sensitive nature and commercial value of such data make them vulnerable to security risks like unauthorized access, piracy, and tampering. To protect the confidentiality and integrity of remote sensing data, researchers have explored various encryption techniques, including traditional ciphers, chaotic mapping, and deep learning. While some progress has been made, there is still a lack of comprehensive end-to-end solutions that effectively address the security challenges in remote sensing data distribution and utilization.
This research proposes a novel encryption scheme that leverages facial features to enhance security and facilitate seamless interactions between users and distribution endpoints, providing a new perspective for the secure dissemination and application of remote sensing data. The study integrates facial information throughout the entire remote sensing data encryption process. Experimental results demonstrate strong encryption efficiency and reconstruction quality. To advance this field, future research should explore the integration of biometric features, blockchain technology, and edge computing into remote sensing data encryption schemes. By doing so, researchers can develop more efficient and robust solutions, promoting the eventual secure application of remote sensing data in various fields and fully unleashing its scientific, commercial, and social potential.