Real-Time Target Detection Architecture Based on Reduced Complexity Hyperspectral Processing

This paper presents a real-time target detection architecture for hyperspectral image processing. The architecture is based on a reduced complexity algorithm for high-throughput applications.We propose an efficient pipelined processing element architecture and a scalable multiple-processing element...

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Main Authors: We-Duke Cho, Sangjin Hong, Shung Han Cho, Kyoung-Su Park
Format: Article
Language:English
Published: SpringerOpen 2008-06-01
Series:EURASIP Journal on Advances in Signal Processing
Online Access:http://dx.doi.org/10.1155/2008/438051
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author We-Duke Cho
Sangjin Hong
Shung Han Cho
Kyoung-Su Park
author_facet We-Duke Cho
Sangjin Hong
Shung Han Cho
Kyoung-Su Park
author_sort We-Duke Cho
collection DOAJ
description This paper presents a real-time target detection architecture for hyperspectral image processing. The architecture is based on a reduced complexity algorithm for high-throughput applications.We propose an efficient pipelined processing element architecture and a scalable multiple-processing element architecture by exploiting data partitioning. We present a processing unit modeling based on the data reduction algorithm in hyperspectral image processing and propose computing structure, that is, to optimize memory usage and eliminates memory bottleneck. We investigate the interconnection topology for the multipleprocessing element architecture to improve the speed. The proposed architecture is designed and implemented in FPGA to illustrate the relationship between hardware complexity and execution throughput of hyperspectral image processing for target detection.
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spelling doaj.art-c38ac218259f486cb2f2dc835cc76ffc2022-12-22T03:53:02ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802008-06-01200810.1155/2008/438051Real-Time Target Detection Architecture Based on Reduced Complexity Hyperspectral ProcessingWe-Duke ChoSangjin HongShung Han ChoKyoung-Su ParkThis paper presents a real-time target detection architecture for hyperspectral image processing. The architecture is based on a reduced complexity algorithm for high-throughput applications.We propose an efficient pipelined processing element architecture and a scalable multiple-processing element architecture by exploiting data partitioning. We present a processing unit modeling based on the data reduction algorithm in hyperspectral image processing and propose computing structure, that is, to optimize memory usage and eliminates memory bottleneck. We investigate the interconnection topology for the multipleprocessing element architecture to improve the speed. The proposed architecture is designed and implemented in FPGA to illustrate the relationship between hardware complexity and execution throughput of hyperspectral image processing for target detection.http://dx.doi.org/10.1155/2008/438051
spellingShingle We-Duke Cho
Sangjin Hong
Shung Han Cho
Kyoung-Su Park
Real-Time Target Detection Architecture Based on Reduced Complexity Hyperspectral Processing
EURASIP Journal on Advances in Signal Processing
title Real-Time Target Detection Architecture Based on Reduced Complexity Hyperspectral Processing
title_full Real-Time Target Detection Architecture Based on Reduced Complexity Hyperspectral Processing
title_fullStr Real-Time Target Detection Architecture Based on Reduced Complexity Hyperspectral Processing
title_full_unstemmed Real-Time Target Detection Architecture Based on Reduced Complexity Hyperspectral Processing
title_short Real-Time Target Detection Architecture Based on Reduced Complexity Hyperspectral Processing
title_sort real time target detection architecture based on reduced complexity hyperspectral processing
url http://dx.doi.org/10.1155/2008/438051
work_keys_str_mv AT wedukecho realtimetargetdetectionarchitecturebasedonreducedcomplexityhyperspectralprocessing
AT sangjinhong realtimetargetdetectionarchitecturebasedonreducedcomplexityhyperspectralprocessing
AT shunghancho realtimetargetdetectionarchitecturebasedonreducedcomplexityhyperspectralprocessing
AT kyoungsupark realtimetargetdetectionarchitecturebasedonreducedcomplexityhyperspectralprocessing