Satellite Image Compression and Denoising With Neural Networks
Vinícius Alves de Oliveira, Marie Chabert, Thomas Oberlin, Charly Poulliat, Mickaël Bruno, Christophe Latry, Mikaël Carlavan, Simon Henrot, Frédéric Falzon, Roberto Camarero
Abstract
Earth observation through satellite images is crucial to help economic activities as well as to monitor the impact of human activities on ecosystems. Current satellite systems are subjected to strong computational complexity constraints. Thus, image compression is performed onboard with specifically tailored algorithms while image denoising is performed on the ground. In this letter, we intend to address satellite image compression and denoising with neural networks. The first proposed approach uses a single neural architecture for joint onboard compression and denoising. The second proposed approach sequentially uses a first neural architecture for onboard compression and a second one for on ground denoising. For both approaches, the onboard architectures are lightened as much as possible, following the procedure proposed by Alves de Oliveira <i>et al.</i> (2021). The two approaches are shown to outperform the current satellite imaging system and their respective pros and cons are discussed.