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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2021-01-01
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Series: | Wireless Communications and Mobile Computing |
Online Access: | http://dx.doi.org/10.1155/2021/6630865 |
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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 |
_version_ |
1714867142556385280 |