Feature fusion for inverse synthetic aperture radar image classification via learning shared hidden space

Abstract Multi‐sensor fusion recognition is a meaningful task in ISAR image recognition. Compared with a single sensor, multi‐sensor fusion can provide richer target information, which is conducive to more accurate and robust identification. However, previous deep learning‐based fusion methods do no...

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Autores principales:	Wenhao Lin, Xunzhang Gao
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/982ad391af2947dc9064a06a82077124
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id	oai:doaj.org-article:982ad391af2947dc9064a06a82077124
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spelling	oai:doaj.org-article:982ad391af2947dc9064a06a820771242021-12-03T08:34:31ZFeature fusion for inverse synthetic aperture radar image classification via learning shared hidden space1350-911X0013-519410.1049/ell2.12311https://doaj.org/article/982ad391af2947dc9064a06a820771242021-12-01T00:00:00Zhttps://doi.org/10.1049/ell2.12311https://doaj.org/toc/0013-5194https://doaj.org/toc/1350-911XAbstract Multi‐sensor fusion recognition is a meaningful task in ISAR image recognition. Compared with a single sensor, multi‐sensor fusion can provide richer target information, which is conducive to more accurate and robust identification. However, previous deep learning‐based fusion methods do not effectively deal with the redundancy and complementarity of information between different sources. In this letter, we construct a shared hidden space to align features from different sources. Accordingly, we design an end‐to‐end deep fusion framework to fuse dual ISAR images at the feature level. For combining the multi‐source information, deep generalised canonical correlation analysis (DGCCA) is used as the loss item to map features extracted from dual input onto the shared hidden space. Moreover, we propose an efficient and lightweight spatial attention module, named united attention module, which can be embedded between dual‐stream convolutional neural networks (CNNs) to promote DGCCA optimisation by information interaction. Compared with other deep fusion frameworks, our model obtains the competitive performance in ISAR image fusion for classification.Wenhao LinXunzhang GaoWileyarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElectronics Letters, Vol 57, Iss 25, Pp 986-988 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 Wenhao Lin Xunzhang Gao Feature fusion for inverse synthetic aperture radar image classification via learning shared hidden space
description	Abstract Multi‐sensor fusion recognition is a meaningful task in ISAR image recognition. Compared with a single sensor, multi‐sensor fusion can provide richer target information, which is conducive to more accurate and robust identification. However, previous deep learning‐based fusion methods do not effectively deal with the redundancy and complementarity of information between different sources. In this letter, we construct a shared hidden space to align features from different sources. Accordingly, we design an end‐to‐end deep fusion framework to fuse dual ISAR images at the feature level. For combining the multi‐source information, deep generalised canonical correlation analysis (DGCCA) is used as the loss item to map features extracted from dual input onto the shared hidden space. Moreover, we propose an efficient and lightweight spatial attention module, named united attention module, which can be embedded between dual‐stream convolutional neural networks (CNNs) to promote DGCCA optimisation by information interaction. Compared with other deep fusion frameworks, our model obtains the competitive performance in ISAR image fusion for classification.
format	article
author	Wenhao Lin Xunzhang Gao
author_facet	Wenhao Lin Xunzhang Gao
author_sort	Wenhao Lin
title	Feature fusion for inverse synthetic aperture radar image classification via learning shared hidden space
title_short	Feature fusion for inverse synthetic aperture radar image classification via learning shared hidden space
title_full	Feature fusion for inverse synthetic aperture radar image classification via learning shared hidden space
title_fullStr	Feature fusion for inverse synthetic aperture radar image classification via learning shared hidden space
title_full_unstemmed	Feature fusion for inverse synthetic aperture radar image classification via learning shared hidden space
title_sort	feature fusion for inverse synthetic aperture radar image classification via learning shared hidden space
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/982ad391af2947dc9064a06a82077124
work_keys_str_mv	AT wenhaolin featurefusionforinversesyntheticapertureradarimageclassificationvialearningsharedhiddenspace AT xunzhanggao featurefusionforinversesyntheticapertureradarimageclassificationvialearningsharedhiddenspace
_version_	1718373405412032512

Feature fusion for inverse synthetic aperture radar image classification via learning shared hidden space

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