Quasi Lossless Satellite Image Compression

We describe an end-to-end trainable neural network for satel-lite image compression. The proposed approach builds upon an image compression scheme based on variational auto-encoders with a learned hyperprior that captures depen-dencies in the latent space for entropy coding. We explore this architecture in light of specificities of satellite imaging: processing constraints onboard the satellite (complexity and memory constraints) and quality needed in terms of reconstruction for the processing task on the ground. We explore data augmentation to improve the reconstruction of challenging image patterns. The proposed model outperforms the current standard of lossy image compression onboard satel-lite based on JPEG 2000, as well as the initial hyper-prior architecture designed for natural images.