SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation

It is necessary to recognize the target in the situation of military battlefield monitoring and civilian real-time monitoring. Sparse representation-based SAR image target recognition method uses training samples or feature information to construct an overcomplete dictionary, which will inevitably a...

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Main Authors: XiuXia Ji, Yinan Sun
Format: Article
Language:English
Published: Hindawi-Wiley 2021-01-01
Series:Wireless Communications and Mobile Computing
Online Access:http://dx.doi.org/10.1155/2021/6630865
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spelling doaj-6389392b604c4483865ee6b6b0636b832021-02-15T12:52:48ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772021-01-01202110.1155/2021/66308656630865SAR Image Target Recognition Based on Monogenic Signal and Sparse RepresentationXiuXia Ji0Yinan Sun1Nanjing Vocational College of Information Technology, Nanjing 210023, ChinaWuhan University, School Electronic Information, Wuhan 430072, ChinaIt is necessary to recognize the target in the situation of military battlefield monitoring and civilian real-time monitoring. Sparse representation-based SAR image target recognition method uses training samples or feature information to construct an overcomplete dictionary, which will inevitably affect the recognition speed. In this paper, a method based on monogenic signal and sparse representation is presented for SAR image target recognition. In this method, the extended maximum average correlation height filter is used to train the samples and generate the templates. The monogenic features of the templates are extracted to construct subdictionaries, and the subdictionaries are combined to construct a cascade dictionary. Sparse representation coefficients of the testing samples over the cascade dictionary are calculated by the orthogonal matching tracking algorithm, and recognition is realized according to the energy of the sparse coefficients and voting recognition. The experimental results suggest that the new approach has good results in terms of recognition accuracy and recognition time.http://dx.doi.org/10.1155/2021/6630865
collection DOAJ
language English
format Article
sources DOAJ
author XiuXia Ji
Yinan Sun
spellingShingle XiuXia Ji
Yinan Sun
SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation
Wireless Communications and Mobile Computing
author_facet XiuXia Ji
Yinan Sun
author_sort XiuXia Ji
title SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation
title_short SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation
title_full SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation
title_fullStr SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation
title_full_unstemmed SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation
title_sort sar image target recognition based on monogenic signal and sparse representation
publisher Hindawi-Wiley
series Wireless Communications and Mobile Computing
issn 1530-8669
1530-8677
publishDate 2021-01-01
description It is necessary to recognize the target in the situation of military battlefield monitoring and civilian real-time monitoring. Sparse representation-based SAR image target recognition method uses training samples or feature information to construct an overcomplete dictionary, which will inevitably affect the recognition speed. In this paper, a method based on monogenic signal and sparse representation is presented for SAR image target recognition. In this method, the extended maximum average correlation height filter is used to train the samples and generate the templates. The monogenic features of the templates are extracted to construct subdictionaries, and the subdictionaries are combined to construct a cascade dictionary. Sparse representation coefficients of the testing samples over the cascade dictionary are calculated by the orthogonal matching tracking algorithm, and recognition is realized according to the energy of the sparse coefficients and voting recognition. The experimental results suggest that the new approach has good results in terms of recognition accuracy and recognition time.
url http://dx.doi.org/10.1155/2021/6630865
work_keys_str_mv AT xiuxiaji sarimagetargetrecognitionbasedonmonogenicsignalandsparserepresentation
AT yinansun sarimagetargetrecognitionbasedonmonogenicsignalandsparserepresentation
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