Unsupervised Change Detection around Subways Based on SAR Combined Difference Images

Jiang, A; Dai, J; Yu, S; Zhang, B; Xie, Q; Zhang, H

Unsupervised Change Detection around Subways Based on SAR Combined Difference Images

Jiang, A Dai, J Yu, S Zhang, B Xie, Q

Zhang, H

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Remote Sensing, 2022, 14, (17)
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Jiang, A
dc.contributor.author	Dai, J
dc.contributor.author	Yu, S
dc.contributor.author	Zhang, B
dc.contributor.author	Xie, Q https://orcid.org/0000-0002-1576-6610
dc.contributor.author	Zhang, H
dc.date.accessioned	2023-02-14T01:58:55Z
dc.date.available	2023-02-14T01:58:55Z
dc.date.issued	2022-09-01
dc.identifier.citation	Remote Sensing, 2022, 14, (17)
dc.identifier.issn	2072-4292
dc.identifier.issn	2072-4292
dc.identifier.uri	http://hdl.handle.net/10453/166099
dc.description.abstract	Prompt and precise acknowledgement of surface change around subways is of considerable significance in urban rail protection and local environmental management. Research has proven the considerable potential of synthetic aperture radar (SAR) images for detecting such information; however, previous studies have mostly focused on change intensity using single Difference images (DIs), e.g., difference value DI (DVDI) and mean value DI (MVDI). With the aim of more accurate information with respect to surface changes around subways, in this study, we proposed a novel SAR detection method that involved three steps: (1) the calculation of three single DIs, (2) the combination of the single DIs and (3) the delineation of the changed area. Compared to existing detection methods, the proposed method represents three major improvements. First, both the intensity information and phase information were applied by combining the DVDI, MVDI and coherent difference images (CDIs). Secondly, a local energy weight (LEW) approach was proposed to combine single DIs instead of the normally used equal weights. Because the changed area often comprises continuous rather than discrete pixels, a combined DI with the LEW (“CoDI-LEW” hereafter) fully considers the attributes of adjacent pixels and enhances the signal-to-noise ratio of SAR images. Thirdly, the FCM algorithm, instead of the widely used threshold methods, was applied to distinguish changed areas from unchanged areas. An experimental comparison with several existing detection methods showed that the proposed method could delineate changed areas with higher accuracy in terms of both quality and quantity. Furthermore, it can effectively execute detection under diverse surface change conditions with good feasibility and applicability.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Remote Sensing
dc.relation.isbasedon	10.3390/rs14174419
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0203 Classical Physics, 0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering
dc.title	Unsupervised Change Detection around Subways Based on SAR Combined Difference Images
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	0203 Classical Physics
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	open_access	*
dc.date.updated	2023-02-14T01:58:54Z
pubs.issue	17
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	17

Abstract:

Prompt and precise acknowledgement of surface change around subways is of considerable significance in urban rail protection and local environmental management. Research has proven the considerable potential of synthetic aperture radar (SAR) images for detecting such information; however, previous studies have mostly focused on change intensity using single Difference images (DIs), e.g., difference value DI (DVDI) and mean value DI (MVDI). With the aim of more accurate information with respect to surface changes around subways, in this study, we proposed a novel SAR detection method that involved three steps: (1) the calculation of three single DIs, (2) the combination of the single DIs and (3) the delineation of the changed area. Compared to existing detection methods, the proposed method represents three major improvements. First, both the intensity information and phase information were applied by combining the DVDI, MVDI and coherent difference images (CDIs). Secondly, a local energy weight (LEW) approach was proposed to combine single DIs instead of the normally used equal weights. Because the changed area often comprises continuous rather than discrete pixels, a combined DI with the LEW (“CoDI-LEW” hereafter) fully considers the attributes of adjacent pixels and enhances the signal-to-noise ratio of SAR images. Thirdly, the FCM algorithm, instead of the widely used threshold methods, was applied to distinguish changed areas from unchanged areas. An experimental comparison with several existing detection methods showed that the proposed method could delineate changed areas with higher accuracy in terms of both quality and quantity. Furthermore, it can effectively execute detection under diverse surface change conditions with good feasibility and applicability.

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http://hdl.handle.net/10453/166099