|Title:||A Scalable and Dynamically Reconfigurable FPGA-Based Embedded System for Real-Time Hyperspectral Unmixing||Authors:||Cervero, Teresa G.
Dondo, Julio Daniel
|UNESCO Clasification:||3307 Tecnología electrónica||Keywords:||Vectors , Hyperspectral imaging , Field programmable gate arrays , Hardware , Scalability , Algorithm design and analysis , Dynamic reconfiguration , Field-Programmable Gate Array (FPGA) , hyperspectral images , linear unmixing , Modified Vertex Component Analysis (MVCA)||Issue Date:||2015||Publisher:||1939-1404||Journal:||IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing||Conference:||6th Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing (WHISPERS)||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.||URI:||http://hdl.handle.net/10553/44400||ISSN:||1939-1404||DOI:||10.1109/JSTARS.2014.2347075||Source:||IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing[ISSN 1939-1404],v. 8 (6897942), p. 2894-2911|
|Appears in Collections:||Artículos|
checked on Nov 26, 2023
WEB OF SCIENCETM
checked on Jul 9, 2023
checked on Sep 2, 2023
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.