A Scalable and Dynamically Reconfigurable FPGA-Based Embedded System for Real-Time Hyperspectral Unmixing

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Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain

Julian.Caba@uclm.es

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Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain

Julio.Dondo@uclm.es

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Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain

Fernando.Rincon@uclm.es

Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain

JuanCarlos.Lopez@uclm.es

  • JOURNAL — Journal of Selected Topics in Applied Earth Observations and Remote Sensing

  • PAGES — 2894-2911

  • ISSN — 1939-1404

  • VOLUME 8

  • PUBLISHER  IEEE

  • YEAR  2015

  • DOI  10.1109/JSTARS.2014.2347075

Cervero, T. G., Caba, J., López, S., Dondo, J. D., Sarmiento, R., Rincón, F., & López, J. (2015). A scalable and dynamically reconfigurable FPGA-based embedded system for real-time hyperspectral unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing8(6), 2894-2911.

@article{cervero2015scalable,
title={A scalable and dynamically reconfigurable FPGA-based embedded system for real-time hyperspectral unmixing},
author={Cervero, Teresa G and Caba, Juli{\'a}n and L{\'o}pez, Sebasti{\'a}n and Dondo, Julio Daniel and Sarmiento, Roberto and Rinc{\'o}n, Fernando and L{\'o}pez, Juan},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
volume={8},
number={6},
pages={2894--2911},
year={2015},
publisher={IEEE}
}

Abstract

Earth observation hyperspectral imaging instruments capture and collect hundreds of different wavelength data corresponding to the same surface. As a result, tons of information must be stored, processed, and transmitted to ground by means of a combination of time-consuming processes. However, one of the requirements of paramount importance when dealing with applications that demand swift responses is the ability to achieve real-time. In this sense, the authors present a flexible and adaptable Field-Programmable Gate Array (FPGA)-based solution for extracting the endmembers of a hyperspectral image according to the Modified Vertex Component Analysis (MVCA) algorithm. The proposed approach is capable of adapting its parallelization execution by scaling the execution in hardware. Thus, the solution uses the dynamic and partial reconfiguration property of FPGAs in order to exploit and vary the level of parallelism at run-time. In order to validate the convenience of using this kind of solutions, the performance of our proposal has been assessed with a set of synthetic images as well as with the well-known Cuprite hyperspectral image. The achieved results demonstrate that the proposed system might be dynamically scaled without significantly affecting total execution times, being able to extract the endmembers of the Cuprite dataset in real-time.

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