Lossless and lossy remote sensing image encryption-compression algorithm based on DeepLabv3+and 2D CS

With the advancement of remote sensing technology, the amount of remote sensing image (RSI) has increased sharply. The explosion in data volume requires higher standards of image compression and encryption technology. This paper proposes a lossless and lossy encryption-compression method for RSI. Because RSIs contain important and unimportant information, using a normal algorithm could ignore their important information or reduce the computational efficiency, so we use the trained model by DeepLabv3+ to segment the region of interest (ROI) and the region of non-interest (RONI), and implement lossless and lossy algorithms respectively. The whole algorithm is based on the newly proposed hyperchaotic system 2D Tent coupled Infinite collapse map (2D TICM). According to the 2D TICM, the dynamic 3D Latin cube is generated. This cube is then used for dynamic scrambling algorithms between multiple planes (3D LMBS), within planes (3D LMIS), and for the multiple dynamic S boxes substitution algorithm. Due to the large size of RSI, the image is divided into blocks, and MATLAB parallel mechanism is used to encrypt each block at the same time. First, the lossy part is scrambled by 3D LMIS, then compressed with 2D compressive sensing (2D CS). Finally, each block is substituted with a different S box. A CS reconstruction algorithm combining decryption and reconstruction, 2D projection gradient chaotic decryption algorithm (2D PG-CD), is proposed. The lossless part embeds the encryption into JPEG-LS, encrypts while compressing, and then encrypts according to 3D LMBS and S boxes. The algorithm in this paper considers the characteristics of RSI, experiments show that it has good security and compression performance.