Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies

Small satellite technologies have enhanced the potential and feasibility of geodesic missions through the simplification of design and decreased costs allowing for more frequent launches. On-satellite data acquisition systems can benefit from the implementation of machine learning (ML) for better performance and greater efficiency on tasks such as image processing or feature extraction. This work presents convolutional autoencoders for implementation on the payload of small satellites, designed to achieve the dual functionality of data compression for more efficient off-satellite transmission and at-source anomaly detection to inform satellite data-taking. This capability is demonstrated for the use case of disaster monitoring using aerial image datasets of the African continent, offering avenues for both the implementation of novel ML-based approaches in small satellite applications and the expansion of space technology and artificial intelligence in Africa.