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A SAR Image Target Recognition Approach via Novel SSF-Net Models

With the wide application of high-resolution radar, the application of Radar Automatic Target Recognition (RATR) is increasingly focused on how to quickly and accurately distinguish high-resolution radar targets. Therefore, Synthetic Aperture Radar (SAR) image recognition technology has become one o...

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Detalles Bibliográficos
Autores principales: Wang, Wei, Zhang, Chengwen, Tian, Jinge, Ou, Jianping, Li, Ji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7368189/
https://www.ncbi.nlm.nih.gov/pubmed/32695155
http://dx.doi.org/10.1155/2020/8859172
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author Wang, Wei
Zhang, Chengwen
Tian, Jinge
Ou, Jianping
Li, Ji
author_facet Wang, Wei
Zhang, Chengwen
Tian, Jinge
Ou, Jianping
Li, Ji
author_sort Wang, Wei
collection PubMed
description With the wide application of high-resolution radar, the application of Radar Automatic Target Recognition (RATR) is increasingly focused on how to quickly and accurately distinguish high-resolution radar targets. Therefore, Synthetic Aperture Radar (SAR) image recognition technology has become one of the research hotspots in this field. Based on the characteristics of SAR images, a Sparse Data Feature Extraction module (SDFE) has been designed, and a new convolutional neural network SSF-Net has been further proposed based on the SDFE module. Meanwhile, in order to improve processing efficiency, the network adopts three methods to classify targets: three Fully Connected (FC) layers, one Fully Connected (FC) layer, and Global Average Pooling (GAP). Among them, the latter two methods have less parameters and computational cost, and they have better real-time performance. The methods were tested on public datasets SAR-SOC and SAR-EOC-1. The experimental results show that the SSF-Net has relatively better robustness and achieves the highest recognition accuracy of 99.55% and 99.50% on SAR-SOC and SAR-EOC-1, respectively, which is 1% higher than the comparison methods on SAR-EOC-1.
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spelling pubmed-73681892020-07-20 A SAR Image Target Recognition Approach via Novel SSF-Net Models Wang, Wei Zhang, Chengwen Tian, Jinge Ou, Jianping Li, Ji Comput Intell Neurosci Research Article With the wide application of high-resolution radar, the application of Radar Automatic Target Recognition (RATR) is increasingly focused on how to quickly and accurately distinguish high-resolution radar targets. Therefore, Synthetic Aperture Radar (SAR) image recognition technology has become one of the research hotspots in this field. Based on the characteristics of SAR images, a Sparse Data Feature Extraction module (SDFE) has been designed, and a new convolutional neural network SSF-Net has been further proposed based on the SDFE module. Meanwhile, in order to improve processing efficiency, the network adopts three methods to classify targets: three Fully Connected (FC) layers, one Fully Connected (FC) layer, and Global Average Pooling (GAP). Among them, the latter two methods have less parameters and computational cost, and they have better real-time performance. The methods were tested on public datasets SAR-SOC and SAR-EOC-1. The experimental results show that the SSF-Net has relatively better robustness and achieves the highest recognition accuracy of 99.55% and 99.50% on SAR-SOC and SAR-EOC-1, respectively, which is 1% higher than the comparison methods on SAR-EOC-1. Hindawi 2020-07-09 /pmc/articles/PMC7368189/ /pubmed/32695155 http://dx.doi.org/10.1155/2020/8859172 Text en Copyright © 2020 Wei Wang et al. http://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
Wang, Wei
Zhang, Chengwen
Tian, Jinge
Ou, Jianping
Li, Ji
A SAR Image Target Recognition Approach via Novel SSF-Net Models
title A SAR Image Target Recognition Approach via Novel SSF-Net Models
title_full A SAR Image Target Recognition Approach via Novel SSF-Net Models
title_fullStr A SAR Image Target Recognition Approach via Novel SSF-Net Models
title_full_unstemmed A SAR Image Target Recognition Approach via Novel SSF-Net Models
title_short A SAR Image Target Recognition Approach via Novel SSF-Net Models
title_sort sar image target recognition approach via novel ssf-net models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7368189/
https://www.ncbi.nlm.nih.gov/pubmed/32695155
http://dx.doi.org/10.1155/2020/8859172
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