Feature Extraction and Fusion for Automatic Target Recognition Based ISAR Images
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/276 |
Resumo: | This paper presents aircraft target recognition (ATR) system using Inverse Synthetic Aperture Radar (ISAR). The methodology used to design the complete processing chain from the acquisition step to the recognition (classification) step is based on the artificial intelligence approach. This process is known as Knowledge Discovery from Data (KDD) which we have adapted to radar target recognition system. We propose a new method for target shape extraction from ISAR images based on the combination of a modified SUSAN Algorithm and Variational of Level Set. To guarantee the invariance in translation and rotation of the extracted shape, the momentinvariants and Fourier descriptors are used. In the second part of this work, We have investigated the impactof the information fusion on our recognition system. Therefore, three combination strategies: probability theory, majority vote and belief theory are applied at score and decision level. The classification results are obtained using Support Vector Machine (SVM) classifier. In the last section, experimental results are provided and discussed. |
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INFOCOMP: Jornal de Ciência da Computação |
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Feature Extraction and Fusion for Automatic Target Recognition Based ISAR ImagesInverse Synthetic Aperture RadarAutomatic Target RecognitionEdges detection and feature vectorsInformation FusionProbability TheoryMajority VoteBelief TheoryThis paper presents aircraft target recognition (ATR) system using Inverse Synthetic Aperture Radar (ISAR). The methodology used to design the complete processing chain from the acquisition step to the recognition (classification) step is based on the artificial intelligence approach. This process is known as Knowledge Discovery from Data (KDD) which we have adapted to radar target recognition system. We propose a new method for target shape extraction from ISAR images based on the combination of a modified SUSAN Algorithm and Variational of Level Set. To guarantee the invariance in translation and rotation of the extracted shape, the momentinvariants and Fourier descriptors are used. In the second part of this work, We have investigated the impactof the information fusion on our recognition system. Therefore, three combination strategies: probability theory, majority vote and belief theory are applied at score and decision level. The classification results are obtained using Support Vector Machine (SVM) classifier. In the last section, experimental results are provided and discussed.Editora da UFLA2009-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/276INFOCOMP Journal of Computer Science; Vol. 8 No. 4 (2009): December, 2009; 1-101982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/276/261Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessNabil, Saidi MohamedAbdelmalek, ToumiAli, KhenchafDriss, AobutajdineBrigitte, Hoeltzener2015-07-22T18:26:28Zoai:infocomp.dcc.ufla.br:article/276Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:29.161163INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
Feature Extraction and Fusion for Automatic Target Recognition Based ISAR Images |
title |
Feature Extraction and Fusion for Automatic Target Recognition Based ISAR Images |
spellingShingle |
Feature Extraction and Fusion for Automatic Target Recognition Based ISAR Images Nabil, Saidi Mohamed Inverse Synthetic Aperture Radar Automatic Target Recognition Edges detection and feature vectors Information Fusion Probability Theory Majority Vote Belief Theory |
title_short |
Feature Extraction and Fusion for Automatic Target Recognition Based ISAR Images |
title_full |
Feature Extraction and Fusion for Automatic Target Recognition Based ISAR Images |
title_fullStr |
Feature Extraction and Fusion for Automatic Target Recognition Based ISAR Images |
title_full_unstemmed |
Feature Extraction and Fusion for Automatic Target Recognition Based ISAR Images |
title_sort |
Feature Extraction and Fusion for Automatic Target Recognition Based ISAR Images |
author |
Nabil, Saidi Mohamed |
author_facet |
Nabil, Saidi Mohamed Abdelmalek, Toumi Ali, Khenchaf Driss, Aobutajdine Brigitte, Hoeltzener |
author_role |
author |
author2 |
Abdelmalek, Toumi Ali, Khenchaf Driss, Aobutajdine Brigitte, Hoeltzener |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Nabil, Saidi Mohamed Abdelmalek, Toumi Ali, Khenchaf Driss, Aobutajdine Brigitte, Hoeltzener |
dc.subject.por.fl_str_mv |
Inverse Synthetic Aperture Radar Automatic Target Recognition Edges detection and feature vectors Information Fusion Probability Theory Majority Vote Belief Theory |
topic |
Inverse Synthetic Aperture Radar Automatic Target Recognition Edges detection and feature vectors Information Fusion Probability Theory Majority Vote Belief Theory |
description |
This paper presents aircraft target recognition (ATR) system using Inverse Synthetic Aperture Radar (ISAR). The methodology used to design the complete processing chain from the acquisition step to the recognition (classification) step is based on the artificial intelligence approach. This process is known as Knowledge Discovery from Data (KDD) which we have adapted to radar target recognition system. We propose a new method for target shape extraction from ISAR images based on the combination of a modified SUSAN Algorithm and Variational of Level Set. To guarantee the invariance in translation and rotation of the extracted shape, the momentinvariants and Fourier descriptors are used. In the second part of this work, We have investigated the impactof the information fusion on our recognition system. Therefore, three combination strategies: probability theory, majority vote and belief theory are applied at score and decision level. The classification results are obtained using Support Vector Machine (SVM) classifier. In the last section, experimental results are provided and discussed. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/276 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/276 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/276/261 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 8 No. 4 (2009): December, 2009; 1-10 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874740904525824 |