Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies
This work addresses data management challenges for small satellite operators, offering a dual-function ML solution that is incremental in applying existing autoencoder methods to a new domain.
The paper tackles the problem of efficient data handling in small satellites by using convolutional autoencoders for data compression and anomaly detection, demonstrating this on aerial image datasets for disaster monitoring in Africa with potential improvements in transmission efficiency and real-time data analysis.
Small satellite technologies have enhanced the potential and feasibility of geodesic missions, through 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 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 a use case of disaster monitoring using aerial image datasets of the African continent, offering avenues for both novel ML-based approaches in small satellite applications along with the expansion of space technology and artificial intelligence in Africa.