On-board image classification payload for a 3U CubeSat using machine learning for on-orbit cloud detection

CubeSats are giving the opportunity for educational institutes to participate in the space industry, develop new technologies and test out new ideas in outer space. CubeSat missions are developed to perform scientific research and demonstrate new space technologies with relatively cheap cost and limited resources. This category of satellites has many limitations such as the short development time, the power consumption and the limited time and capability of data downlink. Earth Observation from a Low Earth Orbit is one of the most appealing m applications of CubeSats developed by students or non-space faring countries. Investigating new technologies to improve image quality and studying ways to increase acquisition adequacy is very promising. This paper aims to introduce a mission hardware design and machine learning-based algorithm used within an Earth Observation (EO) CubeSat. The case study of this paper is Alainsat-1 project which is a 3U CubeSat developed with the support of IEEE Geo-science and Remote Sensing Society (GRSS) at the National Space Science and Technology Center, UAE. The satellite is planned to be launched by 2022. A low-resolution Commercial off-the-shelf (COTS) camera for EO is developed as a primary mission in this CubeSat. The compatible hardware design and software algorithm proposed is responsible for classifying the images captured by the camera into different categories based on cloud intensity detected in these images before downloading them to the ground station. A microcontroller-based architecture is developed for controlling the mission board; it is responsible for accessing the memory, reading the images, and running the cloud detection algorithm. The cloud detection algorithm is based on a U-net architecture while the algorithm is developed using a Tensor-flow library. This model is trained using a dataset of images taken from the Landsat 8 satellite project. Moreover, the SPARCS cloud assessment dataset is used to evaluate the developed model on a new set of images. The overall accuracy achieved by the model is around 85% in addition to the acceptable performance of the model observed on a set of low-resolution images. The plan is to make the design modular and optimize its performance to be used on-board CubeSats fulfilling the size constraint and overall power consumption limitation of an add-on module to a camera mission

1U CubeSatでのバイナリ画像分類用に設計された畳み込みニューラルネットワーク

As of 2020, more than a thousand CubeSats have been launched into space. The nanosatellite standard allowed launch providers to utilize empty spaces in their rockets while giving educational institutions, research facilities and commercial start-up companies the chance to build, test and operate satellites in orbit. This exponential rise in the number of CubeSats has led to an increasing number of diverse missions. Missions on astrobiology, state-of-art technology demonstration, high revisit-time earth observation and space weather have been implemented. In 2018, NASA’s JPL demonstrated CubeSat’s first use in deep space by launching MarCO A and MarCO B. The CubeSats successfully relayed information received from InSight Mars Lander in Mars to Earth. Increasing complexity in missions, however, require increased access to data. Most CubeSats still rely on extremely low data rates for data transfer. Size, Weight and Power (SWaP) requirements for 1U are stringent and rely on VHF/UHF bands for data transmission. Kyushu Institute of Technology’s BIRDS-3 Project has downlink rate of 4800bps and takes about 2-3 days to reconstruct a 640x480 (VGA) image on the ground. Not only is this process extremely time consuming and manual but it also does not guarantee that the image downlinked is usable. There is a need for automatic selection of quality data and improve the work process. The purpose of this research is to design a state-of-art, novel Convolutional Neural Network (CNN) for automated onboard image classification on CubeSats. The CNN is extremely small, efficient, accurate, and versatile. The CNN is trained on a completely new CubeSat image dataset. The CNN is designed to fulfill SWaP requirements of 1U CubeSat so that it can be scaled to fit in bigger satellites in the future. The CNN is tested on never-before-seen BIRDS-3 CubeSat test dataset and is benchmarked against SVM, AE and DBN. The CNN automatizes images selection on-orbit, prioritizes quality data, and cuts down operation time significantly.九州工業大学博士学位論文 学位記番号：工博甲第510号 学位授与年月日：令和2年12月28日1 Introduction|2 Convolutional Neural Networks|3 Methodology|4 Results|5 Conclusion九州工業大学令和2年