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Adaptive Multispectral GPU Accelerated Architecture for Earth Observation Satellites

Davidson, RL and Bridges, CP (2016) Adaptive Multispectral GPU Accelerated Architecture for Earth Observation Satellites In: 2016 IEEE International Conference on Imaging Systems and Techniques (IST 2016), 2016-10-04 - 2016-10-06, Chania, Crete Island, Greece.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Surrey Space Centre
Authors :
NameEmailORCID
Davidson, RLUNSPECIFIEDUNSPECIFIED
Bridges, CPUNSPECIFIEDUNSPECIFIED
Date : 2016
Copyright Disclaimer : © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 09 Nov 2016 08:54
Last Modified : 31 Oct 2017 18:54
URI: http://epubs.surrey.ac.uk/id/eprint/812796

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