Efficient Attributed Scatter Center Extraction Based on Image-Domain Sparse Representation

Yang, D; Ni, W; Du, L; Liu, H; Wang, J

Efficient Attributed Scatter Center Extraction Based on Image-Domain Sparse Representation

Yang, D Ni, W

Du, L Liu, H Wang, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Signal Processing, 2020, 68, pp. 4368-4381
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Yang, D
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Du, L
dc.contributor.author	Liu, H
dc.contributor.author	Wang, J
dc.date.accessioned	2020-12-23T08:09:16Z
dc.date.available	2020-12-23T08:09:16Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Transactions on Signal Processing, 2020, 68, pp. 4368-4381
dc.identifier.issn	1053-587X
dc.identifier.issn	1941-0476
dc.identifier.uri	http://hdl.handle.net/10453/144944
dc.description.abstract	© 1991-2012 IEEE. As an efficient way to interpret the measurements of high-frequency synthetic aperture radar (SAR), an attributed scattering center (ASC) model provides concise and physically relevant features of complex targets. However, accurate extractions of ASCs have been heavily penalized by high memory requirements and computational complexity. We propose to convert SAR measurements to sparse representations in the image domain where the ASC model parameters can be estimated by using an orthogonal matching pursuit (OMP) algorithm or its Newtonlized variation. Two important new properties of the ASC model are unveiled in the image domain, namely, 'translatability' and 'additivity.' The properties can help save the dictionary of OMP from sampling the position and length parameters. The atoms of the dictionary become localized, thereby reducing the dictionary size and accelerating ASC extractions. Extensive experiments are conducted based on open-source XPATCH Backhoe data, measured MSTAR data, and synthetic backscatter data. The results show that the proposed approach is able to outperform existing image-domain algorithms in terms of accuracy and noise resistance, and outperform existing frequency-domain algorithms in terms of memory requirement and runtime.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Signal Processing
dc.relation.isbasedon	10.1109/TSP.2020.3011332
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Networking & Telecommunications
dc.title	Efficient Attributed Scatter Center Extraction Based on Image-Domain Sparse Representation
dc.type	Journal Article
utslib.citation.volume	68
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-12-23T08:08:53Z
pubs.publication-status	Published
pubs.volume	68

Abstract:

© 1991-2012 IEEE. As an efficient way to interpret the measurements of high-frequency synthetic aperture radar (SAR), an attributed scattering center (ASC) model provides concise and physically relevant features of complex targets. However, accurate extractions of ASCs have been heavily penalized by high memory requirements and computational complexity. We propose to convert SAR measurements to sparse representations in the image domain where the ASC model parameters can be estimated by using an orthogonal matching pursuit (OMP) algorithm or its Newtonlized variation. Two important new properties of the ASC model are unveiled in the image domain, namely, 'translatability' and 'additivity.' The properties can help save the dictionary of OMP from sampling the position and length parameters. The atoms of the dictionary become localized, thereby reducing the dictionary size and accelerating ASC extractions. Extensive experiments are conducted based on open-source XPATCH Backhoe data, measured MSTAR data, and synthetic backscatter data. The results show that the proposed approach is able to outperform existing image-domain algorithms in terms of accuracy and noise resistance, and outperform existing frequency-domain algorithms in terms of memory requirement and runtime.

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http://hdl.handle.net/10453/144944