Conceptual low-cost on-board high performance computing in CubeSat nanosatellites for pattern recognition in Earth's remote sensing

Nowadays, remote sensing data taken from artificial satellites require high space com- munications bandwidths as well as high computational processing burdens due to the vertiginous development and specialisation of on-board payloads specifically designed for remote sensing purposes. Nevertheless, these factors become a severe problem when con- sidering nanosatellites, particularly those based in the CubeSat standard, due to the strong limitations that it imposes in volume, power and mass. Thus, the applications of remote sensing in this class of satellites, widely sought due to their affordable cost and easiness of construction and deployment, are very restricted due to their very limited on-board computer power, notwithstanding their Low Earth Orbits (LEO) which make them ideal for Earth’s remote sensing. In this work we present the feasibility of the integration of an NVIDIA GPU of low mass and power as the on-board computer for 1-3U CubeSats. From the remote sensing point of view, we present nine processing-intensive algorithms very commonly used for the processing of remote sensing data which can be executed on-board on this platform. In this sense, we present the performance of these algorithms on the proposed on-board computer with respect with a typical on-board computer for CubeSats (ARM Cortex-A57 MP Core Processor), showing that they have acceleration factors of average of 14.04× ∼14.72× in average. This study sets the precedent to perform satellite on-board high performance computing so to widen the remote sensing capabilities of CubeSats.