Remote Sensing Image Encryption Leveraging Unimodular Transformation and Galois Field

In modern digital society, securing remote sensing images (RSI) is vital due to their importance in environmental monitoring, agriculture, and disaster management. This research delivers an innovative encryption technique that combines the mathematical elegance of unimodular maps with the robust structure of Galois fields. Our method uses unimodular transformations for excellent area-preserving properties, ensuring high diffusion and confusion in encrypted images. The integration of Galois fields allows for efficient and secure arithmetic operations, paving the way for complex transformations that safeguard sensitive data. Through rigorous experimental analysis, we demonstrate the effectiveness of our encryption scheme, highlighting its resilience against various cryptographic attacks and exceptional key sensitivity. The findings suggest that this approach improves the security of RSI. The proposed methodology achieved secure transmission of RSI and was found to be more effective than other similar schemes with an entropy of 7.9997 (close to 8) and few other parameters used for comparison. It also raises data protection standards in a more susceptible digital world. This breakthrough in cryptography remote sensing promises to protect sensitive data with remarkable efficiency and dependability.