Compression Sensing Satellite Image Pixel Scrambling Scheme using Unique Seed Generation for Intra-Block Confusion with LP Rotation Mechanism

Without image compression, encrypted satellite image remains too large and cumbersome, making timely transmission to ground stations impractical. During transmission from space to the ground station, satellite images undergo compression to reduce bandwidth usage and encryption to safeguard data integrity and prevent unauthorized access. The unauthorized access of satellite imagery poses significant risks, including security breaches, misinformation, and compromised decision-making. To protect the integrity and confidentiality of critical geospatial data used in defense, disaster management, and environmental monitoring, robust satellite image encryption is imperative. Existing algorithms often prioritize security at the expense of processing speed or data fidelity. This paper introduces a versatile scheme for the compression and encryption of satellite images, structured in three distinct phases. In the first phase, satellite images are divided into blocks, generating unique initial conditions (seeds) for each block as security keys using the chaotic Sin map. These conditions are subsequently utilized by blockwise independent Tent Maps to produce random chaotic coefficients, enabling complex pixel scrambling through an XOR-based confusion approach. In the second phase, remote sensing images are compressed using the first-level Lifting Wavelet Transform (LWT1), maintaining image fidelity. In the third phase, blockwise rotation is achieved using Lehmer PRNG (LP) to generate random numbers for circular pixel shifts, followed by classical RSA encryption applied to the rotated blocks for secure transmission. The proposed algorithm is lightweight, offering low computational complexity that is suitable for satellite systems and other imaging applications. The SinCrypTent encryption model provides a vast key space, effectively resisting brute force and other cyberattacks. Empirical validation of the model includes differential attack analysis, correlation analysis, entropy analysis, and comparative evaluation with recent state-of-the-art algorithms, demonstrating its superior efficacy in ensuring secure and efficient satellite image encryption.