Autonomy for remote sensing &amp;#x2014; Experiences from the IPEX CubeSat

Doubleday, Joshua; Chien, Steve; Norton, Charles D.; Wagstaff, Kiri L.; Thompson, David R.; Bellardo, John; Francis, C. R.; Baumgarten, Eric

doi:10.1109/igarss.2015.7327033

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…It is exciting that sensor design has also moved toward smaller, solid state systems requiring less power for deployment on small satellites (Doubleday et al, 2015;Bender et al, 2018), aircraft, autonomous unoccupied aerial systems (UAS) (Wu et al, 2019), or small watercraft dedicated to specific problems (Klemas, 2015;Ackleson et al, 2017). This represents an emerging dimension to how modern remote sensing data are acquired.…”

Section: Suborbital Systemsmentioning

confidence: 99%

Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook

Dierssen

Ackleson

Joyce

et al. 2021

Front. Environ. Sci.

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Intensifying pressure on global aquatic resources and services due to population growth and climate change is inspiring new surveying technologies to provide science-based information in support of management and policy strategies. One area of rapid development is hyperspectral remote sensing: imaging across the full spectrum of visible and infrared light. Hyperspectral imagery contains more environmentally meaningful information than panchromatic or multispectral imagery and is poised to provide new applications relevant to society, including assessments of aquatic biodiversity, habitats, water quality, and natural and anthropogenic hazards. To aid in these advances, we provide resources relevant to hyperspectral remote sensing in terms of providing the latest reviews, databases, and software available for practitioners in the field. We highlight recent advances in sensor design, modes of deployment, and image analysis techniques that are becoming more widely available to environmental researchers and resource managers alike. Systems recently deployed on space- and airborne platforms are presented, as well as future missions and advances in unoccupied aerial systems (UAS) and autonomous in-water survey methods. These systems will greatly enhance the ability to collect interdisciplinary observations on-demand and in previously inaccessible environments. Looking forward, advances in sensor miniaturization are discussed alongside the incorporation of citizen science, moving toward open and FAIR (findable, accessible, interoperable, and reusable) data. Advances in machine learning and cloud computing allow for exploitation of the full electromagnetic spectrum, and better bridging across the larger scientific community that also includes biogeochemical modelers and climate scientists. These advances will place sophisticated remote sensing capabilities into the hands of individual users and provide on-demand imagery tailored to research and management requirements, as well as provide critical input to marine and climate forecasting systems. The next decade of hyperspectral aquatic remote sensing is on the cusp of revolutionizing the way we assess and monitor aquatic environments and detect changes relevant to global communities.

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Section: Suborbital Systemsmentioning

confidence: 99%

Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook

Dierssen

Ackleson

Joyce

et al. 2021

Front. Environ. Sci.

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“…The capability of filtering out images that do not meet certain criteria (data reduction) can greatly increase the potentiality of CubeSats, as improvements in sensors generate increasingly larger data volumes and storage space on CubeSats is typically very limited [54]. Reference [55] describes the validation on a CubeSat of intelligent algorithms to discard images with no information and other autonomous capabilities (e.g., computer vision). Many proposed future missions are supposed to produce enormous volumes of data.…”

Section: Processors To Enable On-board Decision Makingmentioning

confidence: 99%

“…To enable them, Ref. [55] suggests that either significant communication advancements or data reduction techniques are needed. However, advances in both capabilities would provide systems designers with a larger design space where better trade-offs are possible for missions producing large volumes of data.…”

Section: Processors To Enable On-board Decision Makingmentioning

confidence: 99%

Leveraging the Openness and Modularity of RISC-V in Space

Mascio¹,

Menicucci²,

Gill³

et al. 2019

Journal of Aerospace Information Systems

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This paper proposes a roadmap to address present and future needs in space systems with RISC-V processors. RISC-V is an open and modular instruction set architecture, which is rapidly growing in popularity in terrestrial applications. To satisfy different applications with contrasting requirements in satellite data systems, four different types of processors are identified: 1) low-area/low-power microcontrollers, 2) on-board computers, 3) general-purpose processors for payloads, and 4) enhanced payload processors for artificial intelligence. Several solutions based on RISC-V are proposed for each of these types of processors and compared with proprietary commercial-off-the-shelf and spacegrade solutions. An extensive analysis of the results available from literature is conducted to show that RISC-V has the potential to solve such a wide range of needs. This paper will also show the unprecedented number of open-source implementations and models that were developed in a relative short time on a single instruction set architecture. Future space systems could benefit from many of those developments, and this work identifies and highlights what is still missing to satisfy the specific needs of processors for space, especially in terms of fault tolerance and technology readiness level.

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“…IPEX, a 2013 -2015 CubeSat mission funded by NASA's ESTO validated onboard instrument processing, mission planning, and autonomous payload operations to be used on future hyperspectral missions, including for HyspIRI's Intelligent Payload Module (IPM). IPEX demonstrated several technologies for data reduction: computer vision, image analysis, and operations autonomy [5]. As FPGAs in space are susceptible to radiation effects, the use of commodity FPGA devices requires improved fault mitigation techniques.…”

mentioning

confidence: 99%

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

Cole

Wilson

Little

2018

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

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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.

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Autonomy for remote sensing — Experiences from the IPEX CubeSat

Cited by 7 publications

References 16 publications

Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook

Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook

Leveraging the Openness and Modularity of RISC-V in Space

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

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