Foreground feature manifold ranking method for SAR image change detection

There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located...
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Autor*in:	LUO Qingli [verfasserIn] CUI Fengzhi [verfasserIn] WEI Jujie [verfasserIn] MING Lei [verfasserIn]

Format:	E-Artikel
Sprache:	Chinesisch

Erschienen:	2022

Schlagwörter:	sar image change detection foreground feature superpixel manifold ranking

Übergeordnetes Werk:	In: Acta Geodaetica et Cartographica Sinica - Surveying and Mapping Press, 2014, 51(2022), 11, Seite 2365-2378
Übergeordnetes Werk:	volume:51 ; year:2022 ; number:11 ; pages:2365-2378

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.11947/j.AGCS.2022.20200512

Katalog-ID:	DOAJ085463191

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520			\|a There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located at the boundary of the SAR image. In order to avoid the above limitations, a method of manifold ranking based on superpixel segmentation and foreground features for change detection (MRSFCD) was designed. Firstly, the difference image was constructed by weighted fusion of single pixel and neighborhood logarithmic ratio operator, which can maintain consistency within the change areas and suppress noise interference. The difference image was then segmented by the superpixel model. After that, the improved undirected graph connection method of superpixels was proposed. The main idea is that superpixels on the boundary are not considered when connecting, and superpixel segmentation results and grayscale information are applied for three adjacencies. Finally, we do a dot product between the significance image by manifold ranking based on foreground features and the single-pixel logarithmic difference image, and the final binary change image is obtained by threshold segmentation. In this paper, three datasets of dual-phase images are tested. The results indicate that compared with other change detection algorithms, the proposed method can improve the accuracy of change detection effectively.
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650		4	\|a manifold ranking
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allfields	10.11947/j.AGCS.2022.20200512 doi (DE-627)DOAJ085463191 (DE-599)DOAJ2784ba259b9c4466a12127307c4773ed DE-627 ger DE-627 rakwb chi GA1-1776 LUO Qingli verfasserin aut Foreground feature manifold ranking method for SAR image change detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located at the boundary of the SAR image. In order to avoid the above limitations, a method of manifold ranking based on superpixel segmentation and foreground features for change detection (MRSFCD) was designed. Firstly, the difference image was constructed by weighted fusion of single pixel and neighborhood logarithmic ratio operator, which can maintain consistency within the change areas and suppress noise interference. The difference image was then segmented by the superpixel model. After that, the improved undirected graph connection method of superpixels was proposed. The main idea is that superpixels on the boundary are not considered when connecting, and superpixel segmentation results and grayscale information are applied for three adjacencies. Finally, we do a dot product between the significance image by manifold ranking based on foreground features and the single-pixel logarithmic difference image, and the final binary change image is obtained by threshold segmentation. In this paper, three datasets of dual-phase images are tested. The results indicate that compared with other change detection algorithms, the proposed method can improve the accuracy of change detection effectively. sar image change detection foreground feature superpixel manifold ranking Mathematical geography. Cartography CUI Fengzhi verfasserin aut WEI Jujie verfasserin aut MING Lei verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 51(2022), 11, Seite 2365-2378 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:51 year:2022 number:11 pages:2365-2378 https://doi.org/10.11947/j.AGCS.2022.20200512 kostenfrei https://doaj.org/article/2784ba259b9c4466a12127307c4773ed kostenfrei http://xb.sinomaps.com/article/2022/1001-1595/20221113.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 51 2022 11 2365-2378
spelling	10.11947/j.AGCS.2022.20200512 doi (DE-627)DOAJ085463191 (DE-599)DOAJ2784ba259b9c4466a12127307c4773ed DE-627 ger DE-627 rakwb chi GA1-1776 LUO Qingli verfasserin aut Foreground feature manifold ranking method for SAR image change detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located at the boundary of the SAR image. In order to avoid the above limitations, a method of manifold ranking based on superpixel segmentation and foreground features for change detection (MRSFCD) was designed. Firstly, the difference image was constructed by weighted fusion of single pixel and neighborhood logarithmic ratio operator, which can maintain consistency within the change areas and suppress noise interference. The difference image was then segmented by the superpixel model. After that, the improved undirected graph connection method of superpixels was proposed. The main idea is that superpixels on the boundary are not considered when connecting, and superpixel segmentation results and grayscale information are applied for three adjacencies. Finally, we do a dot product between the significance image by manifold ranking based on foreground features and the single-pixel logarithmic difference image, and the final binary change image is obtained by threshold segmentation. In this paper, three datasets of dual-phase images are tested. The results indicate that compared with other change detection algorithms, the proposed method can improve the accuracy of change detection effectively. sar image change detection foreground feature superpixel manifold ranking Mathematical geography. Cartography CUI Fengzhi verfasserin aut WEI Jujie verfasserin aut MING Lei verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 51(2022), 11, Seite 2365-2378 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:51 year:2022 number:11 pages:2365-2378 https://doi.org/10.11947/j.AGCS.2022.20200512 kostenfrei https://doaj.org/article/2784ba259b9c4466a12127307c4773ed kostenfrei http://xb.sinomaps.com/article/2022/1001-1595/20221113.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 51 2022 11 2365-2378
allfields_unstemmed	10.11947/j.AGCS.2022.20200512 doi (DE-627)DOAJ085463191 (DE-599)DOAJ2784ba259b9c4466a12127307c4773ed DE-627 ger DE-627 rakwb chi GA1-1776 LUO Qingli verfasserin aut Foreground feature manifold ranking method for SAR image change detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located at the boundary of the SAR image. In order to avoid the above limitations, a method of manifold ranking based on superpixel segmentation and foreground features for change detection (MRSFCD) was designed. Firstly, the difference image was constructed by weighted fusion of single pixel and neighborhood logarithmic ratio operator, which can maintain consistency within the change areas and suppress noise interference. The difference image was then segmented by the superpixel model. After that, the improved undirected graph connection method of superpixels was proposed. The main idea is that superpixels on the boundary are not considered when connecting, and superpixel segmentation results and grayscale information are applied for three adjacencies. Finally, we do a dot product between the significance image by manifold ranking based on foreground features and the single-pixel logarithmic difference image, and the final binary change image is obtained by threshold segmentation. In this paper, three datasets of dual-phase images are tested. The results indicate that compared with other change detection algorithms, the proposed method can improve the accuracy of change detection effectively. sar image change detection foreground feature superpixel manifold ranking Mathematical geography. Cartography CUI Fengzhi verfasserin aut WEI Jujie verfasserin aut MING Lei verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 51(2022), 11, Seite 2365-2378 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:51 year:2022 number:11 pages:2365-2378 https://doi.org/10.11947/j.AGCS.2022.20200512 kostenfrei https://doaj.org/article/2784ba259b9c4466a12127307c4773ed kostenfrei http://xb.sinomaps.com/article/2022/1001-1595/20221113.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 51 2022 11 2365-2378
allfieldsGer	10.11947/j.AGCS.2022.20200512 doi (DE-627)DOAJ085463191 (DE-599)DOAJ2784ba259b9c4466a12127307c4773ed DE-627 ger DE-627 rakwb chi GA1-1776 LUO Qingli verfasserin aut Foreground feature manifold ranking method for SAR image change detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located at the boundary of the SAR image. In order to avoid the above limitations, a method of manifold ranking based on superpixel segmentation and foreground features for change detection (MRSFCD) was designed. Firstly, the difference image was constructed by weighted fusion of single pixel and neighborhood logarithmic ratio operator, which can maintain consistency within the change areas and suppress noise interference. The difference image was then segmented by the superpixel model. After that, the improved undirected graph connection method of superpixels was proposed. The main idea is that superpixels on the boundary are not considered when connecting, and superpixel segmentation results and grayscale information are applied for three adjacencies. Finally, we do a dot product between the significance image by manifold ranking based on foreground features and the single-pixel logarithmic difference image, and the final binary change image is obtained by threshold segmentation. In this paper, three datasets of dual-phase images are tested. The results indicate that compared with other change detection algorithms, the proposed method can improve the accuracy of change detection effectively. sar image change detection foreground feature superpixel manifold ranking Mathematical geography. Cartography CUI Fengzhi verfasserin aut WEI Jujie verfasserin aut MING Lei verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 51(2022), 11, Seite 2365-2378 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:51 year:2022 number:11 pages:2365-2378 https://doi.org/10.11947/j.AGCS.2022.20200512 kostenfrei https://doaj.org/article/2784ba259b9c4466a12127307c4773ed kostenfrei http://xb.sinomaps.com/article/2022/1001-1595/20221113.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 51 2022 11 2365-2378
allfieldsSound	10.11947/j.AGCS.2022.20200512 doi (DE-627)DOAJ085463191 (DE-599)DOAJ2784ba259b9c4466a12127307c4773ed DE-627 ger DE-627 rakwb chi GA1-1776 LUO Qingli verfasserin aut Foreground feature manifold ranking method for SAR image change detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located at the boundary of the SAR image. In order to avoid the above limitations, a method of manifold ranking based on superpixel segmentation and foreground features for change detection (MRSFCD) was designed. Firstly, the difference image was constructed by weighted fusion of single pixel and neighborhood logarithmic ratio operator, which can maintain consistency within the change areas and suppress noise interference. The difference image was then segmented by the superpixel model. After that, the improved undirected graph connection method of superpixels was proposed. The main idea is that superpixels on the boundary are not considered when connecting, and superpixel segmentation results and grayscale information are applied for three adjacencies. Finally, we do a dot product between the significance image by manifold ranking based on foreground features and the single-pixel logarithmic difference image, and the final binary change image is obtained by threshold segmentation. In this paper, three datasets of dual-phase images are tested. The results indicate that compared with other change detection algorithms, the proposed method can improve the accuracy of change detection effectively. sar image change detection foreground feature superpixel manifold ranking Mathematical geography. Cartography CUI Fengzhi verfasserin aut WEI Jujie verfasserin aut MING Lei verfasserin aut In Acta Geodaetica et Cartographica Sinica Surveying and Mapping Press, 2014 51(2022), 11, Seite 2365-2378 (DE-627)57517014X (DE-600)2445687-1 10011595 nnns volume:51 year:2022 number:11 pages:2365-2378 https://doi.org/10.11947/j.AGCS.2022.20200512 kostenfrei https://doaj.org/article/2784ba259b9c4466a12127307c4773ed kostenfrei http://xb.sinomaps.com/article/2022/1001-1595/20221113.htm kostenfrei https://doaj.org/toc/1001-1595 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 51 2022 11 2365-2378
language	Chinese
source	In Acta Geodaetica et Cartographica Sinica 51(2022), 11, Seite 2365-2378 volume:51 year:2022 number:11 pages:2365-2378
sourceStr	In Acta Geodaetica et Cartographica Sinica 51(2022), 11, Seite 2365-2378 volume:51 year:2022 number:11 pages:2365-2378
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	sar image change detection foreground feature superpixel manifold ranking Mathematical geography. Cartography
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authorswithroles_txt_mv	LUO Qingli @@aut@@ CUI Fengzhi @@aut@@ WEI Jujie @@aut@@ MING Lei @@aut@@
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callnumber-first	G - Geography, Anthropology, Recreation
author	LUO Qingli
spellingShingle	LUO Qingli misc GA1-1776 misc sar image misc change detection misc foreground feature misc superpixel misc manifold ranking misc Mathematical geography. Cartography Foreground feature manifold ranking method for SAR image change detection
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topic_title	GA1-1776 Foreground feature manifold ranking method for SAR image change detection sar image change detection foreground feature superpixel manifold ranking
topic	misc GA1-1776 misc sar image misc change detection misc foreground feature misc superpixel misc manifold ranking misc Mathematical geography. Cartography
topic_unstemmed	misc GA1-1776 misc sar image misc change detection misc foreground feature misc superpixel misc manifold ranking misc Mathematical geography. Cartography
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title	Foreground feature manifold ranking method for SAR image change detection
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title_full	Foreground feature manifold ranking method for SAR image change detection
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title_sort	foreground feature manifold ranking method for sar image change detection
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title_auth	Foreground feature manifold ranking method for SAR image change detection
abstract	There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located at the boundary of the SAR image. In order to avoid the above limitations, a method of manifold ranking based on superpixel segmentation and foreground features for change detection (MRSFCD) was designed. Firstly, the difference image was constructed by weighted fusion of single pixel and neighborhood logarithmic ratio operator, which can maintain consistency within the change areas and suppress noise interference. The difference image was then segmented by the superpixel model. After that, the improved undirected graph connection method of superpixels was proposed. The main idea is that superpixels on the boundary are not considered when connecting, and superpixel segmentation results and grayscale information are applied for three adjacencies. Finally, we do a dot product between the significance image by manifold ranking based on foreground features and the single-pixel logarithmic difference image, and the final binary change image is obtained by threshold segmentation. In this paper, three datasets of dual-phase images are tested. The results indicate that compared with other change detection algorithms, the proposed method can improve the accuracy of change detection effectively.
abstractGer	There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located at the boundary of the SAR image. In order to avoid the above limitations, a method of manifold ranking based on superpixel segmentation and foreground features for change detection (MRSFCD) was designed. Firstly, the difference image was constructed by weighted fusion of single pixel and neighborhood logarithmic ratio operator, which can maintain consistency within the change areas and suppress noise interference. The difference image was then segmented by the superpixel model. After that, the improved undirected graph connection method of superpixels was proposed. The main idea is that superpixels on the boundary are not considered when connecting, and superpixel segmentation results and grayscale information are applied for three adjacencies. Finally, we do a dot product between the significance image by manifold ranking based on foreground features and the single-pixel logarithmic difference image, and the final binary change image is obtained by threshold segmentation. In this paper, three datasets of dual-phase images are tested. The results indicate that compared with other change detection algorithms, the proposed method can improve the accuracy of change detection effectively.
abstract_unstemmed	There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located at the boundary of the SAR image. In order to avoid the above limitations, a method of manifold ranking based on superpixel segmentation and foreground features for change detection (MRSFCD) was designed. Firstly, the difference image was constructed by weighted fusion of single pixel and neighborhood logarithmic ratio operator, which can maintain consistency within the change areas and suppress noise interference. The difference image was then segmented by the superpixel model. After that, the improved undirected graph connection method of superpixels was proposed. The main idea is that superpixels on the boundary are not considered when connecting, and superpixel segmentation results and grayscale information are applied for three adjacencies. Finally, we do a dot product between the significance image by manifold ranking based on foreground features and the single-pixel logarithmic difference image, and the final binary change image is obtained by threshold segmentation. In this paper, three datasets of dual-phase images are tested. The results indicate that compared with other change detection algorithms, the proposed method can improve the accuracy of change detection effectively.
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title_short	Foreground feature manifold ranking method for SAR image change detection
url	https://doi.org/10.11947/j.AGCS.2022.20200512 https://doaj.org/article/2784ba259b9c4466a12127307c4773ed http://xb.sinomaps.com/article/2022/1001-1595/20221113.htm https://doaj.org/toc/1001-1595
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