Hyperspectral Data Processing: an Opportunity for End-To-End Processing

The evolution and improvements in hyperspectral instrumentation are being matched by information technology improvements in science data processing and analysis. Research has improved techniques in both onboard and ground-based processing to support other high data volume instruments. Algorithms and hardware have evolved, permitting faster access to the observations. Cloud computing is taking the algorithms to the data. Technologies are being specifically designed to address high volume data sets and are an investment in the improvement of hyperspectral data processing

畳み込みニューラルネットワークアプローチを使用した山火事検出のための宇宙イメージングの前処理と後処理

An increasing number of wildfire cases every year has caused fear around the world. Scientists and researchers agreed that this catastrophe occurred due to climate change. Dry and windy conditions had worsened the situation in the affected area. Properties and life losses have created serious concerns for the authority to find a solution for preparing and fighting the fire promptly. Since the late ‘70s, leveraging satellite technology has brought helpful insight to monitor, detect, and assess wildfire events. NOAA AVHRR is one of the oldest Earth Observation (EO) satellites with the main objective of detecting and mapping forest fires. The MODIS fire product regularly upgrades the sensor technology and launches the satellites into space. However, with the advancement of current technologies, a miniaturized satellite called CubeSat creates a novel mission design by reducing the satellite development time, increasing the launching batch in a constellation method, and enhancing the detection result wildfire. The prime limitations of CubeSat are the size, weight, and power (SWaP), which lead to the optimization design of the payload and the communication subsystem. The big image data acquired by the CubeSat creates a bottleneck effect between the satellite and the ground station due to the low downlink data rate. Deep learning (DL) techniques are improving in the computer vision area. Image classification, detection, and segmentation are used in neural network architecture designed by artificial intelligence researchers. In this study, the convolution neural network (CNN) algorithm was chosen for the pre-processing onboard CubeSat for wildfire detection as well as for the graphical user interface (GUI) used on the ground post-processing. The first and crucial step was to develop a custom dataset for wildfire images by leveraging satellite imagery. Defining the specifications of the CubeSat payload to which the CNN was implemented could support selecting the accurate resolution and bands for acquiring the satellite images. The KITSUNE satellite is a 6-unit CubeSat platform implementing the CNN onboard for wildfire image classification. It serves as the secondary mission to support the main mission of a 5-m class EO. The on-ground testing revealed that the CNN could classify wildfire occurrences on the satellite system using the MiniVGGNet network with an overall accuracy of 98 % and an F1-score of 97% success rate in 137 seconds. Other models were also compared, such as ResNet and MiniGoogLeNet implemented on the GUI with 97% and 96% F1-score, respectively. Overall, this research showed the feasibility of CubeSat of executing CNN onboard in orbit, particularly for wildfire detection.九州工業大学博士学位論文 学位記番号：工博甲第556号 学位授与年月日：令和4年9月26日1: Introduction|2: Research Background and Literature Reviews|3: Research Methodology|4: Results|5: Discussion|6: Conclusion and Recommendation九州工業大学令和4年

Artificial Intelligence for Small Satellites Mission Autonomy

Space mission engineering has always been recognized as a very challenging and innovative branch of engineering: since the beginning of the space race, numerous milestones, key successes and failures, improvements, and connections with other engineering domains have been reached. Despite its relative young age, space engineering discipline has not gone through homogeneous times: alternation of leading nations, shifts in public and private interests, allocations of resources to different domains and goals are all examples of an intrinsic dynamism that characterized this discipline. The dynamism is even more striking in the last two decades, in which several factors contributed to the fervour of this period. Two of the most important ones were certainly the increased presence and push of the commercial and private sector and the overall intent of reducing the size of the spacecraft while maintaining comparable level of performances. A key example of the second driver is the introduction, in 1999, of a new category of space systems called CubeSats. Envisioned and designed to ease the access to space for universities, by standardizing the development of the spacecraft and by ensuring high probabilities of acceptance as piggyback customers in launches, the standard was quickly adopted not only by universities, but also by agencies and private companies. CubeSats turned out to be a disruptive innovation, and the space mission ecosystem was deeply changed by this. New mission concepts and architectures are being developed: CubeSats are now considered as secondary payloads of bigger missions, constellations are being deployed in Low Earth Orbit to perform observation missions to a performance level considered to be only achievable by traditional, fully-sized spacecraft. CubeSats, and more in general the small satellites technology, had to overcome important challenges in the last few years that were constraining and reducing the diffusion and adoption potential of smaller spacecraft for scientific and technology demonstration missions. Among these challenges were: the miniaturization of propulsion technologies, to enable concepts such as Rendezvous and Docking, or interplanetary missions; the improvement of telecommunication state of the art for small satellites, to enable the downlink to Earth of all the data acquired during the mission; and the miniaturization of scientific instruments, to be able to exploit CubeSats in more meaningful, scientific, ways. With the size reduction and with the consolidation of the technology, many aspects of a space mission are reduced in consequence: among these, costs, development and launch times can be cited. An important aspect that has not been demonstrated to scale accordingly is operations: even for small satellite missions, human operators and performant ground control centres are needed. In addition, with the possibility of having constellations or interplanetary distributed missions, a redesign of how operations are management is required, to cope with the innovation in space mission architectures. The present work has been carried out to address the issue of operations for small satellite missions. The thesis presents a research, carried out in several institutions (Politecnico di Torino, MIT, NASA JPL), aimed at improving the autonomy level of space missions, and in particular of small satellites. The key technology exploited in the research is Artificial Intelligence, a computer science branch that has gained extreme interest in research disciplines such as medicine, security, image recognition and language processing, and is currently making its way in space engineering as well. The thesis focuses on three topics, and three related applications have been developed and are here presented: autonomous operations by means of event detection algorithms, intelligent failure detection on small satellite actuator systems, and decision-making support thanks to intelligent tradespace exploration during the preliminary design of space missions. The Artificial Intelligent technologies explored are: Machine Learning, and in particular Neural Networks; Knowledge-based Systems, and in particular Fuzzy Logics; Evolutionary Algorithms, and in particular Genetic Algorithms. The thesis covers the domain (small satellites), the technology (Artificial Intelligence), the focus (mission autonomy) and presents three case studies, that demonstrate the feasibility of employing Artificial Intelligence to enhance how missions are currently operated and designed

Pathfinding Interplanetary Bus Capability for the Cal Poly CubeSat Laboratory Through the Development of a Phobos-Deimos Mission Concept

With the rise of CubeSats and the demonstration of their many space applications, there is interest in interplanetary CubeSats to act for example as scientific investigations or communications relays. In line with the increasing demand for this class of small satellites, the Cal Poly CubeSat Lab (CPCL) seeks to develop a bus that could support an interplanetary science payload. To facilitate this, a mission concept to conduct science of the moons of Mars, Phobos and Deimos, is investigated by determining the mission needs for a CubeSat in a Phobos-Deimos cycler orbit through the development of a baseline design to meet mission objectives. This baseline design is then compared by subsystem to CPCL’s current capabilities to identify technology, facility, and knowledge gaps and recommend a path forward to close them. The resulting baseline design is a 16U bus capable of transferring from an initial low Mars orbit to a Phobos-Deimos cycler orbit using a combined chemical and electric propulsion system. The bus is designed for a 3.5 year mission lifetime collecting radiation data and images, utilizing a relay architecture to downlink payload data. Estimates for mass, volume, and power available for an additional payload are up to 2.3 kg in ~4U with power consumption up to 13 to 38 W. This baseline requires further iteration due to non-closure of the thermal protection subsystem and improvement of other subsystems but serves as a starting point for exploration into CPCL’s next steps in becoming an interplanetary bus provider. Major subsystem areas identified for hardware performance improvement within CPCL are propulsion, communications, power, and mechanisms

Pushing the Boundaries of Spacecraft Autonomy and Resilience with a Custom Software Framework and Onboard Digital Twin

This research addresses the high CubeSat mission failure rates caused by inadequate software and overreliance on ground control. By applying a reliable design methodology to flight software development and developing an onboard digital twin platform with fault prediction capabilities, this study provides a solution to increase satellite resilience and autonomy, thus reducing the risk of mission failure. These findings have implications for spacecraft of all sizes, paving the way for more resilient space missions

3rd International Workshop on Instrumentation for Planetary Missions : October 24–27, 2016, Pasadena, California

The purpose of this workshop is to provide a forum for collaboration, exchange of ideas and information, and discussions in the area of the instruments, subsystems, and other payload-related technologies needed to address planetary science questions. The agenda will compose a broad survey of the current state-of-the-art and emerging capabilities in instrumentation available for future planetary missions.Universities Space Research Association (USRA); Lunar and Planetary Institute (LPI); Jet Propulsion Laboratory (JPL)Conveners: Sabrina Feldman, Jet Propulsion Laboratory, David Beaty, Jet Propulsion Laboratory ; Science Organizing Committee: Carlton Allen, Johnson Space Center (retired) [and 12 others