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A Multiview SAR Target Recognition Method Using Inner Correlation Analysis

This paper proposes a synthetic aperture radar (SAR) image target recognition method using multiple views and inner correlation analysis. Due to the azimuth sensitivity of SAR images, the inner correlation between multiview images participating in recognition is not stable enough. To this end, the p...

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Detalles Bibliográficos
Autores principales: Lei, Lei, Guo, Dongen, Feng, Zhihui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635931/
https://www.ncbi.nlm.nih.gov/pubmed/34868301
http://dx.doi.org/10.1155/2021/9703709
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author Lei, Lei
Guo, Dongen
Feng, Zhihui
author_facet Lei, Lei
Guo, Dongen
Feng, Zhihui
author_sort Lei, Lei
collection PubMed
description This paper proposes a synthetic aperture radar (SAR) image target recognition method using multiple views and inner correlation analysis. Due to the azimuth sensitivity of SAR images, the inner correlation between multiview images participating in recognition is not stable enough. To this end, the proposed method first clusters multiview SAR images based on image correlation and nonlinear correlation information entropy (NCIE) in order to obtain multiple view sets with strong internal correlations. For each view set, the multitask sparse representation is used to reconstruct the SAR images in it to obtain high-precision reconstructions. Finally, the linear weighting method is used to fuse the reconstruction errors from different view sets and the target category is determined according to the fusion error. In the experiment, the tests are conducted based on the MSTAR dataset, and the results validate the effectiveness of the proposed method.
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spelling pubmed-86359312021-12-02 A Multiview SAR Target Recognition Method Using Inner Correlation Analysis Lei, Lei Guo, Dongen Feng, Zhihui Comput Intell Neurosci Research Article This paper proposes a synthetic aperture radar (SAR) image target recognition method using multiple views and inner correlation analysis. Due to the azimuth sensitivity of SAR images, the inner correlation between multiview images participating in recognition is not stable enough. To this end, the proposed method first clusters multiview SAR images based on image correlation and nonlinear correlation information entropy (NCIE) in order to obtain multiple view sets with strong internal correlations. For each view set, the multitask sparse representation is used to reconstruct the SAR images in it to obtain high-precision reconstructions. Finally, the linear weighting method is used to fuse the reconstruction errors from different view sets and the target category is determined according to the fusion error. In the experiment, the tests are conducted based on the MSTAR dataset, and the results validate the effectiveness of the proposed method. Hindawi 2021-11-24 /pmc/articles/PMC8635931/ /pubmed/34868301 http://dx.doi.org/10.1155/2021/9703709 Text en Copyright © 2021 Lei Lei et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Lei, Lei
Guo, Dongen
Feng, Zhihui
A Multiview SAR Target Recognition Method Using Inner Correlation Analysis
title A Multiview SAR Target Recognition Method Using Inner Correlation Analysis
title_full A Multiview SAR Target Recognition Method Using Inner Correlation Analysis
title_fullStr A Multiview SAR Target Recognition Method Using Inner Correlation Analysis
title_full_unstemmed A Multiview SAR Target Recognition Method Using Inner Correlation Analysis
title_short A Multiview SAR Target Recognition Method Using Inner Correlation Analysis
title_sort multiview sar target recognition method using inner correlation analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635931/
https://www.ncbi.nlm.nih.gov/pubmed/34868301
http://dx.doi.org/10.1155/2021/9703709
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