Airborne demonstration of FPGA implementation of Fast Lossless hyperspectral data compression system

Keymeulen, Didier; Aranki, Nazeeh; Bakhshi, A.K.; Luong, Huy; Sarture, Charles M.; Dolman, David

doi:10.1109/ahs.2014.6880188

Cited by 29 publications

(22 citation statements)

References 7 publications

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“…The limit is set at 122.60 MSa/s/W with our Virtex-4 version (based on XPE estimations). Ignoring estimations, Keymeulen et al's [14] version, at 57.10 MSa/s/W, is the most efficient one. In any case, both significantly outperform the most efficient GPU version, at 3.96 MSa/s/W.…”

Section: Platformmentioning

confidence: 99%

“…This way, the specification can be mapped to different FPGA models by simply changing the target platform. FPGA implementations of this algorithm have already been presented in [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…This slowed down the algorithm enough that real-time compression was not possible for the newest sensors. All three [12,14,15] achieve real-time compression. However, they also use external RAM for intermediate storage, which introduces two downfalls in a spatial setting: the implementation needs more power to work, and it is more prone to errors since bit flips [16] in both the RAM and the FPGA can affect the output.…”

Section: Introductionmentioning

confidence: 99%

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Parallel Implementation of the CCSDS 1.2.3 Standard for Hyperspectral Lossless Compression

2017

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Hyperspectral imaging is a technology which, by sensing hundreds of wavelengths per pixel, enables fine studies of the captured objects. This produces great amounts of data that require equally big storage, and compression with algorithms such as the Consultative Committee for Space Data Systems (CCSDS) 1.2.3 standard is a must. However, the speed of this lossless compression algorithm is not enough in some real-time scenarios if we use a single-core processor. This is where architectures such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) can shine best. In this paper, we present both FPGA and OpenCL implementations of the CCSDS 1.2.3 algorithm. The proposed paralellization method has been implemented on the Virtex-7 XC7VX690T, Virtex-5 XQR5VFX130 and Virtex-4 XC2VFX60 FPGAs, and on the GT440 and GT610 GPUs, and tested using hyperspectral data from NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Both approaches fulfill our real-time requirements. This paper attempts to shed some light on the comparison between both approaches, including other works from existing literature, explaining the trade-offs of each one.

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Section: Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parallel Implementation of the CCSDS 1.2.3 Standard for Hyperspectral Lossless Compression

2017

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“…The main objective is to establish a Recommended Standard for a multispectral and hyperspectral images, and to specify the compressed data format. In literature, many proposed approaches implement the recommendations of the CCSDS 123 standard for the lossless compression of hyperspectral images, as for instance, the ones described in [9][10][11].…”

Section: Related Workmentioning

confidence: 99%

Multiband and Lossless Compression of Hyperspectral Images

Pizzolante

Carpentieri

2016

Algorithms

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Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.). We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images.

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“…Two of these papers detail findings, from work contracted by the JPL NASA, on the efficient GPU implementation of CCSDS-123 for airborne hyperspectral imagers [27] [28]. Although the CCSDS-123 algorithm was originally designed for efficient FPGA hardware implementation, it was shown that an effective parallel GPU implementation of the algorithm can be devised.…”

Section: Gpu Optimised Onboard Image Processing Softwarementioning

confidence: 99%

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

Davidson

Bridges

2016

2016 IEEE International Conference on Imaging Systems and Techniques (IST)

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Abstract-In recent years the growth in quantity, diversity and capability of Earth Observation (EO) satellites, has enabled increase's in the achievable payload data dimensionality and volume. However, the lack of equivalent advancement in downlink technology has resulted in the development of an onboard data bottleneck. This bottleneck must be alleviated in order for EO satellites to continue to efficiently provide high quality and increasing quantities of payload data.This research explores the selection and implementation of state-of-the-art multidimensional image compression algorithms and proposes a new onboard data processing architecture, to help alleviate the bottleneck and increase the data throughput of the platform. The proposed new system is based upon a backplane architecture to provide scalability with different satellite platform sizes and varying mission's objectives. The heterogeneous nature of the architecture allows benefits of both Field Programmable Gate Array (FPGA) and Graphical Processing Unit (GPU) hardware to be leveraged for maximised data processing throughput.

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Airborne demonstration of FPGA implementation of Fast Lossless hyperspectral data compression system

Cited by 29 publications

References 7 publications

Parallel Implementation of the CCSDS 1.2.3 Standard for Hyperspectral Lossless Compression

Parallel Implementation of the CCSDS 1.2.3 Standard for Hyperspectral Lossless Compression

Multiband and Lossless Compression of Hyperspectral Images

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

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