High Performance Space Computing with System-on-Chip Instrument Avionics for Space-based Next Generation Imaging Spectrometers (NGIS)

Keymeulen, Didier; Shin, Simon; Riddley, Jason; Klimesh, M.; Kiely, Aaron; Liggett, Elliott; Sullivan, Peter; Bernas, Michael; Ghossemi, Hamid; Flesch, G.; Cheng, Michael; Dolinar, S.; Dolman, David; Roth, Kevin; Holyoake, Chris; Crocker, Ken; Smith, Adam

doi:10.1109/ahs.2018.8541473

Cited by 15 publications

(9 citation statements)

References 7 publications

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“…Devices requiring less power are also available, such as Xilinx Spartan, Altera Cyclone, and Xilinx Artix families, with power requirements ranging from 1 to 2.5 Watts [ 9 ]. Among the SoC platforms covered in this review, the largest required power was reached by Xilinx Zynq Z7045Q SoC at 9 Watts [ 109 ]. Comparing this to the GPU power requirements, it is reported that, for instance, the Nvidia GPUs require power that ranges between 75 and 365 Watts [ 162 ].…”

Section: Discussionmentioning

confidence: 99%

“…References [ 103 , 104 , 105 , 106 , 107 , 108 , 109 ] cover Fast Lossless (FL). FL is a prediction-based algorithm developed by Jet Propulsion Laboratory (JPL).…”

Section: Hardware-accelerated Compression Algorithms Of Hsimentioning

confidence: 99%

“…The algorithm uses adaptive filtering and exploits redundancy in all three dimensions, spatially and spectrally. Three IP cores of FLEX are integrated into the Zynq SoC device producing a compression rate of 70 frames per second (Fps) benchmarked with the AVIRIS Hawaii hyperspectral image [ 109 ].…”

Section: Hardware-accelerated Compression Algorithms Of Hsimentioning

confidence: 99%

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A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Altamimi

Youssef

2021

Sensors

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Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

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Section: Discussionmentioning

confidence: 99%

“…References [ 103 , 104 , 105 , 106 , 107 , 108 , 109 ] cover Fast Lossless (FL). FL is a prediction-based algorithm developed by Jet Propulsion Laboratory (JPL).…”

Section: Hardware-accelerated Compression Algorithms Of Hsimentioning

confidence: 99%

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A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Altamimi

Youssef

2021

Sensors

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“…The ScOSA Flight Experiment will use the Zynq-7000 SoC [28] as high-performance nodes. This COTS component is often used for space missions nowadays [14,15,22], but only a few evaluations of its failure rates in space have so far been conducted [27]. Instead, there exist some studies of failure rates in ground-based particle accelerators [2,6,17,25,29].…”

Section: Cots In Spacementioning

confidence: 99%

ScOSA system software: the reliable and scalable middleware for a heterogeneous and distributed on-board computer architecture

et al. 2021

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Designing on-board computers (OBC) for future space missions is determined by the trade-off between reliability and performance. Space applications with higher computational demands are not supported by currently available, state-of-the-art, space-qualified computing hardware, since their requirements exceed the capabilities of these components. Such space applications include Earth observation with high-resolution cameras, on-orbit real-time servicing, as well as autonomous spacecraft and rover missions on distant celestial bodies. An alternative to state-of-the-art space-qualified computing hardware is the use of commercial-off-the-shelf (COTS) components for the OBC. Not only are these components cheap and widely available, but they also achieve high performance. Unfortunately, they are also significantly more vulnerable to errors induced by radiation than space-qualified components. The ScOSA (Scalable On-board Computing for Space Avionics) Flight Experiment project aims to develop an OBC architecture which avoids this trade-off by combining space-qualified radiation-hardened components (the reliable computing nodes, RCNs) together with COTS components (the high performance nodes, HPNs) into a single distributed system. To abstract this heterogeneous architecture for the application developers, we are developing a middleware for the aforementioned OBC architecture. Besides providing an monolithic abstraction of the distributed system, the middleware shall also enhance the architecture by providing additional reliability and fault tolerance. In this paper, we present the individual components comprising the middleware, alongside the features the middleware offers. Since the ScOSA Flight Experiment project is a successor of the OBC-NG and the ScOSA projects, its middleware is also a further development of the existing middleware. Therefore, we will present and discuss our contributions and plans for enhancement of the middleware in the course of the current project. Finally, we will present first results for the scalability of the middleware, which we obtained by conducting software-in-the-loop experiments of different sized scenarios.

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“…This work extends upon our previous work [30] by considering a wider selection of spectral decorrelation transforms, compression algorithms, and test scenes. This provides a more complete analysis of the state-of-the-art and increases the significance of the obtained results Note that, even though hardware implementations have been described for some of the aforementioned algorithms (see, e.g., in [31][32][33]), this study focuses on software implementations. This approach is consistent with the current trend in Earth Observation, in which constellations of small satellites and cubesats (mainly from the private sector) are being deployed [2].…”

Section: Introductionmentioning

confidence: 99%

High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation

et al. 2020

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The capacity of the downlink channel is a major bottleneck for applications based on remote sensing hyperspectral imagery (HSI). Data compression is an essential tool to maximize the amount of HSI scenes that can be retrieved on the ground. At the same time, energy and hardware constraints of spaceborne devices impose limitations on the complexity of practical compression algorithms. To avoid any distortion in the analysis of the HSI data, only lossless compression is considered in this study. This work aims at finding the most advantageous compression–complexity trade-off within the state of the art in HSI compression. To do so, a novel comparison of the most competitive spectral decorrelation approaches combined with the best performing low-complexity compressors of the state is presented. Compression performance and execution time results are obtained for a set of 47 HSI scenes produced by 14 different sensors in real remote sensing missions. Assuming only a limited amount of energy is available, obtained data suggest that the FAPEC algorithm yields the best trade-off. When compared to the CCSDS 123.0-B-2 standard, FAPEC is 5.0 times faster and its compressed data rates are on average within 16% of the CCSDS standard. In scenarios where energy constraints can be relaxed, CCSDS 123.0-B-2 yields the best average compression results of all evaluated methods.

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High Performance Space Computing with System-on-Chip Instrument Avionics for Space-based Next Generation Imaging Spectrometers (NGIS)

Cited by 15 publications

References 7 publications

A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

ScOSA system software: the reliable and scalable middleware for a heterogeneous and distributed on-board computer architecture

High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation

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