Cloud Detection Autonomous System Based on Machine Learning and COTS Components On-Board Small Satellites

Abstract

One of the main applications of small satellites is Earth observation. CubeSats and different kinds of nanosatellites usually form constellations that obtain images mainly using an optical payload. There is a massive amount of data generated by these satellites and a limited capacity of download due to volume and mass constraints that make it difficult to use high-speed communication systems and high-power systems. For this reason, it is important to develop satellites with the autonomy to process data on board. In this way, the limited communication channel can be used efficiently to download relevant images containing the required information. In this paper, a system for the satellite on-board processing of RGB images is proposed, which automatically detects the cloud coverage level to prioritize the images and effectively uses the download time and the mission operation center. The system implements a Convolutional Neural Network (CNN) on a Commercial off-the-Shelf (COTS) microcontroller that receives the image and returns the cloud level (priority). After training, the system was tested on a dataset of 100 images with an accuracy of 0.9 and it was also evaluated with CubeSat images to evaluate the performance of a different image sensor. This implementation contributes to the development of autonomous satellites with processing on board.

Keywords

• machine learning
• TinyML
• convolutional neural networks
• on-board processing
• edge processing
• CubeSat