Commercially Available Imaging Payloads for CubeSat Earth Observation Missions

Tomio, Hannah; Thieu, Albert; Gagnon, Amelia; Vlahakis, Sophia K.; Kacker, Shreeyam; Kusters, Joe; Cahoy, Kerri

doi:10.1109/aero53065.2022.9843446

Cited by 3 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of these small satellite missions include SeaHawk-1 and SeaHawk-2 from the University of North Carolina-Wilmington, Spectral Ocean Color (SPOC) by the University of Georgia and the BeaverCube-2 (BC-2) from the Massachusetts Institute of Technology (MIT). 5 Also the ESA's PhiSat-1 mission, which aims to demonstrate the feasibility of onboard processing by including a power-constrained machine learning accelerator and software for custom applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Convolutional variational autoencoders for secure lossy image compression in remote sensing

Giuliano,

Gadsden,

Hilal

et al. 2024

Sensors and Systems for Space Applications XVII

View full text Add to dashboard Cite

The volume of remote sensing data is experiencing rapid growth, primarily due to the plethora of space and air platforms equipped with an array of sensors. Due to limited hardware and battery constraints the data is transmitted back to Earth for processing. The large amounts of data along with security concerns call for new compression and encryption techniques capable of preserving reconstruction quality while minimizing the transmission cost of this data back to Earth. This study investigates image compression based on convolutional variational autoencoders (CVAE), which are capable of substantially reducing the volume of transmitted data while guaranteeing secure lossy image reconstruction. CVAEs have been demonstrated to outperform conventional compression methods such as JPEG2000 by a substantial margin on compression benchmark datasets. The proposed model draws on the strength of the CVAE's capability to abstract data into highly insightful latent spaces, and combining it with the utilization of an entropy bottleneck is capable of finding an optimal balance between compressibility and reconstruction quality. The balance is reached by optimizing over a composite loss function that represents the rate-distortion curve.

show abstract

Section: Introductionmentioning

confidence: 99%

“…To overcome these limitations, onboard processing has emerged as a viable solution to reduce the amount of data that needs to be transmitted. 5 Variational Autoencoders (VAEs) have shown great potential in the field of image compression. VAE's can efficiently encode and decode images while preserving important features.…”

Section: Introductionmentioning

confidence: 99%

Convolutional variational autoencoders for secure lossy image compression in remote sensing

Giuliano,

Gadsden,

Hilal

et al. 2024

Sensors and Systems for Space Applications XVII

View full text Add to dashboard Cite

show abstract

“…Several missions, such as Earth, [2][3][4] and planetary 5 observation and On Orbit Servicing, 6,7 require increasingly high accuracy and pointing stability to allow payloads to acquire valuable data and images. 8 In this context, the attitude determination and control system (ADCS) plays a fundamental role because enable the capability to achieve and maintain a desired target or a subject pointing. Since the design of the ADCS for small satellites often involves low-cost hardware that provides poor performance, it is necessary to employ advanced algorithmic solutions based on effective algorithms to meet the stringent requirements on the attitude knowledge and the attitude and velocity control performance.…”

Section: Introductionmentioning

confidence: 99%

“…This increment is also due to the improvement of the operational capabilities 1 of this kind of satellites supported by the availability of new miniaturized technologies. Several missions, such as Earth, 2–4 and planetary 5 observation and On Orbit Servicing, 6,7 require increasingly high accuracy and pointing stability to allow payloads to acquire valuable data and images 8 . In this context, the attitude determination and control system (ADCS) plays a fundamental role because enable the capability to achieve and maintain a desired target or a subject pointing.…”

Section: Introductionmentioning

confidence: 99%

Novel extended Kalman filter + H∞$$ {\mathbf{H}}_{\infty } $$ optimal output feedback control configuration for small satellites with high pointing stability requirements

Pecorilla,

Stesina

2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

SummaryOne of the most challenging subsystems for the CubeSats is the attitude determination and control system (ADCS) because it involves hardware and software integration, advanced strategies and algorithms to determine and control the attitude that impacts on space maneuvering and well working of a large set of payload (e.g., the optical payload). Although ADCS is largely studied, it still requires further investigations and analysis in order to achieve high‐performance and a reduced efforts. This paper presents an effective and reliable solution for the ADCS of CubeSats involved in Earth observation missions. The solution is based on an innovative framework that leverages the strengths of two distinct methodologies—H‐infinity optimal output feedback control and the extended Kalman filter (EKF)‐to significantly enhance the pointing stability of satellite systems. While H‐infinity optimal output feedback control is traditionally associated with partial state knowledge, we intentionally extend its application to scenarios with complete state information. The study includes a comparative performance analysis involving three algorithm configurations: the classic combination of EKF and model predictive control (MPC), the utilization of H‐infinity optimal output feedback control without EKF, and our proposed integrated approach. Performance evaluations are based on extrinsic indicators, demonstrating the advantages of our method in terms of pointing stability and overall control system performance. This research underscore the significance of combining innovative thinking with well‐established methodologies, unlocking new possibilities for stability enhancement in a range of engineering applications.

show abstract

Bentayga-I: Development of a Low-Cost and Open-Source Multispectral CubeSat for Marine Environment Monitoring and Prevention

Rodríguez-Molina,

Santana,

Machado

et al. 2024

Sensors

View full text Add to dashboard Cite

CubeSats have emerged as a promising alternative to satellite missions for studying remote areas where satellite data are scarce and insufficient, such as coastal and marine environments. However, their standard size and weight limitations make integrating remote sensing optical instruments challenging. This work presents the development of Bentayga-I, a CubeSat designed to validate PANDORA, a self-made, lightweight, cost-effective multispectral camera with interchangeable spectral optical filters, in near-space conditions. Its four selected spectral bands are relevant for ocean studies. Alongside the camera, Bentayga-I integrates a power system for short-time operation capacity; a thermal subsystem to maintain battery function; environmental sensors to monitor the CubeSat’s internal and external conditions; and a communication subsystem to transmit acquired data to a ground station. The first helium balloon launch with B2Space proved that Bentayga-I electronics worked correctly in near-space environments. During this launch, the spectral capabilities of PANDORA alongside the spectrum were validated using a hyperspectral camera. Its scientific applicability was also tested by capturing images of coastal areas. A second launch is planned to further validate the multispectral camera in a real-world scenario. The integration of Bentayga-I and PANDORA presents promising results for future low-cost CubeSats missions.

show abstract

Commercially Available Imaging Payloads for CubeSat Earth Observation Missions

Cited by 3 publications

References 15 publications

Convolutional variational autoencoders for secure lossy image compression in remote sensing

Convolutional variational autoencoders for secure lossy image compression in remote sensing

Novel extended Kalman filter + H∞$$ {\mathbf{H}}_{\infty } $$ optimal output feedback control configuration for small satellites with high pointing stability requirements

Bentayga-I: Development of a Low-Cost and Open-Source Multispectral CubeSat for Marine Environment Monitoring and Prevention

Contact Info

Product

Resources

About