On-board image compression using convolutional autoencoder: performance analysis and application scenarios

The amount of raw data generated by instruments on board Earth Observation (EO) satellites is quite often more than what can be transmitted to the ground, so new advanced onboard processing procedures are required. Artificial Intelligence (AI) and Deep Learning (DL) can provide advanced information from EO data and thanks to specific hardware platforms these algorithms can be used also in space. We present here the Convolutional AutoEncoder (CAE)-based algorithm developed for on-board lossy image compression of the European Space Agency (ESA) Phi-Sat-2 mission. DL algorithms have already been successfully applied for image compression however performance degradation may occur in the context of a representative onboard environment. Therefore, besides analyzing the results for the local hardware environment, we investigate the performance variation for the on-board setting. Moreover, we introduced an applicative metric for the evaluation of the compression to assess the applicability of the reconstructed images for other tasks.