Increasing Downlink Efficiency for Fly-By Imaging Missions Through Convolutional Neural Network-Based Data Reduction

Data transmission requirements are a major constraint for mission design and can increase mission complexity significantly. Thus, reducing the amount of data required to be transmitted is key. In this work, the reference scenario of the European Space Agency’s Comet Interceptor mission, specifically its Optical Periscopic Imager for Comets (OPIC) instrument, is used to assess the possibilities for onboard data selection through convolutional neural networks. In this study, we train various semantic segmentation and object detection networks to automatically determine the most scientifically interesting area on a fly-by image of a comet, focusing on the nucleus and inner coma, and investigate the impact this could have on data reduction. In the context of computational complexity, the average dice coefficient dropped by 0.07 between the best performing and the smallest network for semantic segmentation and by 0.11 for detection networks. While this drop is significant, the more computationally complex networks did not lead to any significant accuracy improvement. Based on the results, we can conclude that using convolutional neural networks is a feasible strategy for reducing data budgets in comet fly-by missions and that even the simplest segmentation networks tested can achieve a meaningful performance, showing that this approach is even feasible on hardware that can be compatible for launch or has already been used in space.