Novel Polarimetric Contrast Enhancement Method Based on Minimal Clutter to Signal Ratio Subspace

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

Novel Polarimetric Contrast Enhancement Method Based on Minimal Clutter to Signal Ratio Subspace

Yang, D Du, L Liu, H Ni, W

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2019, 57, (11), pp. 8570-8583
Issue Date:: 2019-11-01

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Field	Value	Language
dc.contributor.author	Yang, D
dc.contributor.author	Du, L
dc.contributor.author	Liu, H
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.date.accessioned	2021-03-10T03:59:37Z
dc.date.available	2021-03-10T03:59:37Z
dc.date.issued	2019-11-01
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2019, 57, (11), pp. 8570-8583
dc.identifier.issn	0196-2892
dc.identifier.issn	1558-0644
dc.identifier.uri	http://hdl.handle.net/10453/146985
dc.description.abstract	© 1980-2012 IEEE. Enhancing the contrast of target and clutter is a crucial issue in synthetic aperture radar (SAR) image target detection. In this paper, we define a novel subspace, called minimal clutter-to-signal ratio (MCSR) subspace, which can minimize the clutter-to-signal ratio (CSR) by projecting the feature vector to the subspace. Based on MCSR, a novel polarimetric contrast enhancement method is proposed. The MCSR subspace is learned based on the commonly used polarimetric feature vectors extracted from the labeled training SAR image pixels. The feature vectors extracted form candidate SAR image pixels are projected to the MCSR subspace. By calculating the square norm of each transformed feature vector, an enhanced image can be obtained. It is demonstrated that the existing optimization of polarimetric contrast enhancement (OPCE) is a special case of the proposed method to some extent. Experimental results show that our method outperforms the traditional OPCE method on the RadarSat-2 SAR data.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing
dc.relation.isbasedon	10.1109/TGRS.2019.2921629
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2019 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.subject	0404 Geophysics, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Novel Polarimetric Contrast Enhancement Method Based on Minimal Clutter to Signal Ratio Subspace
dc.type	Journal Article
utslib.citation.volume	57
utslib.for	0404 Geophysics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-10T03:59:35Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	57
utslib.citation.issue	11

Abstract:

© 1980-2012 IEEE. Enhancing the contrast of target and clutter is a crucial issue in synthetic aperture radar (SAR) image target detection. In this paper, we define a novel subspace, called minimal clutter-to-signal ratio (MCSR) subspace, which can minimize the clutter-to-signal ratio (CSR) by projecting the feature vector to the subspace. Based on MCSR, a novel polarimetric contrast enhancement method is proposed. The MCSR subspace is learned based on the commonly used polarimetric feature vectors extracted from the labeled training SAR image pixels. The feature vectors extracted form candidate SAR image pixels are projected to the MCSR subspace. By calculating the square norm of each transformed feature vector, an enhanced image can be obtained. It is demonstrated that the existing optimization of polarimetric contrast enhancement (OPCE) is a special case of the proposed method to some extent. Experimental results show that our method outperforms the traditional OPCE method on the RadarSat-2 SAR data.

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http://hdl.handle.net/10453/146985