Q-SAT-GEN - Hybrid generative modelling for satellite image denoising and infilling

Satellite data provides opportunities for governments and businesses around the world in scientific, socio-economic management and commercial applications. Access to timely, reliable, and actionable information is increasingly critical to a growing number of organizations and decision makers who rely on earth observation data.

Gaining useful insights from satellite images can be difficult due to sources of noise such as sensor processing errors, clouds or atmospheric perturbations, the low spatial resolution of typical satellite images, and revisit times on the order of a few hours to days. Holes or gaps in of missing pixel information introduces uncertainty and affects decision making capabilities of stakeholders who rely on accurate information for near-real-time monitoring and inference purposes.

Traditionally, classical generative AI methods such as GANs or diffusion models have been used to address these issues. However, these methods require significant computational resources and suffer from issues such as mode collapse in GANs.

ORCA Computing will deliver a hybrid quantum/classical generative algorithm for satellite image processing both to reduce the computational resources required to train models and to improve their performance. This algorithm uses ORCA's PT-Series quantum processor and unique software stack. This solution will be beneficial for satellite monitoring purposes in areas such as climate and weather monitoring, defence, the environment and agriculture.