Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images

Ground-to-aerial feature matching bridges information from cross-view images, which enables optimized urban applications, e.g., pixel-level geolocating and complete urban 3-D reconstruction. However, urban ground and aerial images typically suffer from drastic changes in viewpoint, scale, and illumi...
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Autor*in:	Xianwei Zheng [verfasserIn] Hongjie Li [verfasserIn] Hanjiang Xiong [verfasserIn] Xiao Xie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Aerial oblique imagery feature matching ground imagery ground-to-aerial image matching repetitive pattern

Übergeordnetes Werk:	In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 14(2021), Seite 4737-4752
Übergeordnetes Werk:	volume:14 ; year:2021 ; pages:4737-4752

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JSTARS.2021.3069222

Katalog-ID:	DOAJ056461259

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520			\|a Ground-to-aerial feature matching bridges information from cross-view images, which enables optimized urban applications, e.g., pixel-level geolocating and complete urban 3-D reconstruction. However, urban ground and aerial images typically suffer from drastic changes in viewpoint, scale, and illumination, together with repetitive patterns. Thus, direct matching of local features between ground and aerial images is particularly difficult because of the low similarity of local descriptors and high ambiguity in true–false match discrimination. For this challenging task, we propose a novel lattice-point mutually guided matching (LPMG) method in this article. We specifically address two key issues: 1) reducing descriptor variance and 2) enhancing true–false match discriminability. The former is solved by recovering the geometry and appearance of the underlying image region in 3-D through automatic view rectification on ground and aerial images. The latter is circumvented by replacing the conventional mismatch removal with an LPMG strategy. In this strategy, the topology structure of repeated façade elements (i.e., lattice), and the high reliable point matching seeds, are first extracted from the rectified ground and aerial images. Then, the point matching seeds guide the self-similar lattice tiles from two views to be precisely aligned, thereby estimating an accurate transformation model from lattice tile correspondences. Finally, the estimated model powerfully supervises the differentiation of true and false matches from the entire putative match set. Extensive experiments conducted on several datasets show that our method can obtain a considerable number of nearly pure correct matches from urban ground and aerial images, significantly outperforming those existing methods.
650		4	\|a Aerial oblique imagery
650		4	\|a feature matching
650		4	\|a ground imagery
650		4	\|a ground-to-aerial image matching
650		4	\|a repetitive pattern
653		0	\|a Ocean engineering
653		0	\|a Geophysics. Cosmic physics
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700	0		\|a Hanjiang Xiong \|e verfasserin \|4 aut
700	0		\|a Xiao Xie \|e verfasserin \|4 aut
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spelling	10.1109/JSTARS.2021.3069222 doi (DE-627)DOAJ056461259 (DE-599)DOAJ57836b6831b6414ba53f976b3b567b73 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Xianwei Zheng verfasserin aut Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ground-to-aerial feature matching bridges information from cross-view images, which enables optimized urban applications, e.g., pixel-level geolocating and complete urban 3-D reconstruction. However, urban ground and aerial images typically suffer from drastic changes in viewpoint, scale, and illumination, together with repetitive patterns. Thus, direct matching of local features between ground and aerial images is particularly difficult because of the low similarity of local descriptors and high ambiguity in true–false match discrimination. For this challenging task, we propose a novel lattice-point mutually guided matching (LPMG) method in this article. We specifically address two key issues: 1) reducing descriptor variance and 2) enhancing true–false match discriminability. The former is solved by recovering the geometry and appearance of the underlying image region in 3-D through automatic view rectification on ground and aerial images. The latter is circumvented by replacing the conventional mismatch removal with an LPMG strategy. In this strategy, the topology structure of repeated façade elements (i.e., lattice), and the high reliable point matching seeds, are first extracted from the rectified ground and aerial images. Then, the point matching seeds guide the self-similar lattice tiles from two views to be precisely aligned, thereby estimating an accurate transformation model from lattice tile correspondences. Finally, the estimated model powerfully supervises the differentiation of true and false matches from the entire putative match set. Extensive experiments conducted on several datasets show that our method can obtain a considerable number of nearly pure correct matches from urban ground and aerial images, significantly outperforming those existing methods. Aerial oblique imagery feature matching ground imagery ground-to-aerial image matching repetitive pattern Ocean engineering Geophysics. Cosmic physics Hongjie Li verfasserin aut Hanjiang Xiong verfasserin aut Xiao Xie verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4737-4752 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4737-4752 https://doi.org/10.1109/JSTARS.2021.3069222 kostenfrei https://doaj.org/article/57836b6831b6414ba53f976b3b567b73 kostenfrei https://ieeexplore.ieee.org/document/9387538/ kostenfrei https://doaj.org/toc/2151-1535 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_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 4737-4752
allfields_unstemmed	10.1109/JSTARS.2021.3069222 doi (DE-627)DOAJ056461259 (DE-599)DOAJ57836b6831b6414ba53f976b3b567b73 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Xianwei Zheng verfasserin aut Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ground-to-aerial feature matching bridges information from cross-view images, which enables optimized urban applications, e.g., pixel-level geolocating and complete urban 3-D reconstruction. However, urban ground and aerial images typically suffer from drastic changes in viewpoint, scale, and illumination, together with repetitive patterns. Thus, direct matching of local features between ground and aerial images is particularly difficult because of the low similarity of local descriptors and high ambiguity in true–false match discrimination. For this challenging task, we propose a novel lattice-point mutually guided matching (LPMG) method in this article. We specifically address two key issues: 1) reducing descriptor variance and 2) enhancing true–false match discriminability. The former is solved by recovering the geometry and appearance of the underlying image region in 3-D through automatic view rectification on ground and aerial images. The latter is circumvented by replacing the conventional mismatch removal with an LPMG strategy. In this strategy, the topology structure of repeated façade elements (i.e., lattice), and the high reliable point matching seeds, are first extracted from the rectified ground and aerial images. Then, the point matching seeds guide the self-similar lattice tiles from two views to be precisely aligned, thereby estimating an accurate transformation model from lattice tile correspondences. Finally, the estimated model powerfully supervises the differentiation of true and false matches from the entire putative match set. Extensive experiments conducted on several datasets show that our method can obtain a considerable number of nearly pure correct matches from urban ground and aerial images, significantly outperforming those existing methods. Aerial oblique imagery feature matching ground imagery ground-to-aerial image matching repetitive pattern Ocean engineering Geophysics. Cosmic physics Hongjie Li verfasserin aut Hanjiang Xiong verfasserin aut Xiao Xie verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4737-4752 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4737-4752 https://doi.org/10.1109/JSTARS.2021.3069222 kostenfrei https://doaj.org/article/57836b6831b6414ba53f976b3b567b73 kostenfrei https://ieeexplore.ieee.org/document/9387538/ kostenfrei https://doaj.org/toc/2151-1535 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_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 4737-4752
allfieldsGer	10.1109/JSTARS.2021.3069222 doi (DE-627)DOAJ056461259 (DE-599)DOAJ57836b6831b6414ba53f976b3b567b73 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Xianwei Zheng verfasserin aut Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ground-to-aerial feature matching bridges information from cross-view images, which enables optimized urban applications, e.g., pixel-level geolocating and complete urban 3-D reconstruction. However, urban ground and aerial images typically suffer from drastic changes in viewpoint, scale, and illumination, together with repetitive patterns. Thus, direct matching of local features between ground and aerial images is particularly difficult because of the low similarity of local descriptors and high ambiguity in true–false match discrimination. For this challenging task, we propose a novel lattice-point mutually guided matching (LPMG) method in this article. We specifically address two key issues: 1) reducing descriptor variance and 2) enhancing true–false match discriminability. The former is solved by recovering the geometry and appearance of the underlying image region in 3-D through automatic view rectification on ground and aerial images. The latter is circumvented by replacing the conventional mismatch removal with an LPMG strategy. In this strategy, the topology structure of repeated façade elements (i.e., lattice), and the high reliable point matching seeds, are first extracted from the rectified ground and aerial images. Then, the point matching seeds guide the self-similar lattice tiles from two views to be precisely aligned, thereby estimating an accurate transformation model from lattice tile correspondences. Finally, the estimated model powerfully supervises the differentiation of true and false matches from the entire putative match set. Extensive experiments conducted on several datasets show that our method can obtain a considerable number of nearly pure correct matches from urban ground and aerial images, significantly outperforming those existing methods. Aerial oblique imagery feature matching ground imagery ground-to-aerial image matching repetitive pattern Ocean engineering Geophysics. Cosmic physics Hongjie Li verfasserin aut Hanjiang Xiong verfasserin aut Xiao Xie verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4737-4752 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4737-4752 https://doi.org/10.1109/JSTARS.2021.3069222 kostenfrei https://doaj.org/article/57836b6831b6414ba53f976b3b567b73 kostenfrei https://ieeexplore.ieee.org/document/9387538/ kostenfrei https://doaj.org/toc/2151-1535 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_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 4737-4752
allfieldsSound	10.1109/JSTARS.2021.3069222 doi (DE-627)DOAJ056461259 (DE-599)DOAJ57836b6831b6414ba53f976b3b567b73 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Xianwei Zheng verfasserin aut Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Ground-to-aerial feature matching bridges information from cross-view images, which enables optimized urban applications, e.g., pixel-level geolocating and complete urban 3-D reconstruction. However, urban ground and aerial images typically suffer from drastic changes in viewpoint, scale, and illumination, together with repetitive patterns. Thus, direct matching of local features between ground and aerial images is particularly difficult because of the low similarity of local descriptors and high ambiguity in true–false match discrimination. For this challenging task, we propose a novel lattice-point mutually guided matching (LPMG) method in this article. We specifically address two key issues: 1) reducing descriptor variance and 2) enhancing true–false match discriminability. The former is solved by recovering the geometry and appearance of the underlying image region in 3-D through automatic view rectification on ground and aerial images. The latter is circumvented by replacing the conventional mismatch removal with an LPMG strategy. In this strategy, the topology structure of repeated façade elements (i.e., lattice), and the high reliable point matching seeds, are first extracted from the rectified ground and aerial images. Then, the point matching seeds guide the self-similar lattice tiles from two views to be precisely aligned, thereby estimating an accurate transformation model from lattice tile correspondences. Finally, the estimated model powerfully supervises the differentiation of true and false matches from the entire putative match set. Extensive experiments conducted on several datasets show that our method can obtain a considerable number of nearly pure correct matches from urban ground and aerial images, significantly outperforming those existing methods. Aerial oblique imagery feature matching ground imagery ground-to-aerial image matching repetitive pattern Ocean engineering Geophysics. Cosmic physics Hongjie Li verfasserin aut Hanjiang Xiong verfasserin aut Xiao Xie verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 4737-4752 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:4737-4752 https://doi.org/10.1109/JSTARS.2021.3069222 kostenfrei https://doaj.org/article/57836b6831b6414ba53f976b3b567b73 kostenfrei https://ieeexplore.ieee.org/document/9387538/ kostenfrei https://doaj.org/toc/2151-1535 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_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 4737-4752
language	English
source	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14(2021), Seite 4737-4752 volume:14 year:2021 pages:4737-4752
sourceStr	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14(2021), Seite 4737-4752 volume:14 year:2021 pages:4737-4752
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Aerial oblique imagery feature matching ground imagery ground-to-aerial image matching repetitive pattern Ocean engineering Geophysics. Cosmic physics
isfreeaccess_bool	true
container_title	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
authorswithroles_txt_mv	Xianwei Zheng @@aut@@ Hongjie Li @@aut@@ Hanjiang Xiong @@aut@@ Xiao Xie @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	581732634
id	DOAJ056461259
language_de	englisch
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callnumber-first	T - Technology
author	Xianwei Zheng
spellingShingle	Xianwei Zheng misc TC1501-1800 misc QC801-809 misc Aerial oblique imagery misc feature matching misc ground imagery misc ground-to-aerial image matching misc repetitive pattern misc Ocean engineering misc Geophysics. Cosmic physics Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images
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topic_title	TC1501-1800 QC801-809 Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images Aerial oblique imagery feature matching ground imagery ground-to-aerial image matching repetitive pattern
topic	misc TC1501-1800 misc QC801-809 misc Aerial oblique imagery misc feature matching misc ground imagery misc ground-to-aerial image matching misc repetitive pattern misc Ocean engineering misc Geophysics. Cosmic physics
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title	Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images
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title_full	Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images
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title_sort	lattice-point mutually guided ground-to-aerial feature matching for urban scene images
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title_auth	Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images
abstract	Ground-to-aerial feature matching bridges information from cross-view images, which enables optimized urban applications, e.g., pixel-level geolocating and complete urban 3-D reconstruction. However, urban ground and aerial images typically suffer from drastic changes in viewpoint, scale, and illumination, together with repetitive patterns. Thus, direct matching of local features between ground and aerial images is particularly difficult because of the low similarity of local descriptors and high ambiguity in true–false match discrimination. For this challenging task, we propose a novel lattice-point mutually guided matching (LPMG) method in this article. We specifically address two key issues: 1) reducing descriptor variance and 2) enhancing true–false match discriminability. The former is solved by recovering the geometry and appearance of the underlying image region in 3-D through automatic view rectification on ground and aerial images. The latter is circumvented by replacing the conventional mismatch removal with an LPMG strategy. In this strategy, the topology structure of repeated façade elements (i.e., lattice), and the high reliable point matching seeds, are first extracted from the rectified ground and aerial images. Then, the point matching seeds guide the self-similar lattice tiles from two views to be precisely aligned, thereby estimating an accurate transformation model from lattice tile correspondences. Finally, the estimated model powerfully supervises the differentiation of true and false matches from the entire putative match set. Extensive experiments conducted on several datasets show that our method can obtain a considerable number of nearly pure correct matches from urban ground and aerial images, significantly outperforming those existing methods.
abstractGer	Ground-to-aerial feature matching bridges information from cross-view images, which enables optimized urban applications, e.g., pixel-level geolocating and complete urban 3-D reconstruction. However, urban ground and aerial images typically suffer from drastic changes in viewpoint, scale, and illumination, together with repetitive patterns. Thus, direct matching of local features between ground and aerial images is particularly difficult because of the low similarity of local descriptors and high ambiguity in true–false match discrimination. For this challenging task, we propose a novel lattice-point mutually guided matching (LPMG) method in this article. We specifically address two key issues: 1) reducing descriptor variance and 2) enhancing true–false match discriminability. The former is solved by recovering the geometry and appearance of the underlying image region in 3-D through automatic view rectification on ground and aerial images. The latter is circumvented by replacing the conventional mismatch removal with an LPMG strategy. In this strategy, the topology structure of repeated façade elements (i.e., lattice), and the high reliable point matching seeds, are first extracted from the rectified ground and aerial images. Then, the point matching seeds guide the self-similar lattice tiles from two views to be precisely aligned, thereby estimating an accurate transformation model from lattice tile correspondences. Finally, the estimated model powerfully supervises the differentiation of true and false matches from the entire putative match set. Extensive experiments conducted on several datasets show that our method can obtain a considerable number of nearly pure correct matches from urban ground and aerial images, significantly outperforming those existing methods.
abstract_unstemmed	Ground-to-aerial feature matching bridges information from cross-view images, which enables optimized urban applications, e.g., pixel-level geolocating and complete urban 3-D reconstruction. However, urban ground and aerial images typically suffer from drastic changes in viewpoint, scale, and illumination, together with repetitive patterns. Thus, direct matching of local features between ground and aerial images is particularly difficult because of the low similarity of local descriptors and high ambiguity in true–false match discrimination. For this challenging task, we propose a novel lattice-point mutually guided matching (LPMG) method in this article. We specifically address two key issues: 1) reducing descriptor variance and 2) enhancing true–false match discriminability. The former is solved by recovering the geometry and appearance of the underlying image region in 3-D through automatic view rectification on ground and aerial images. The latter is circumvented by replacing the conventional mismatch removal with an LPMG strategy. In this strategy, the topology structure of repeated façade elements (i.e., lattice), and the high reliable point matching seeds, are first extracted from the rectified ground and aerial images. Then, the point matching seeds guide the self-similar lattice tiles from two views to be precisely aligned, thereby estimating an accurate transformation model from lattice tile correspondences. Finally, the estimated model powerfully supervises the differentiation of true and false matches from the entire putative match set. Extensive experiments conducted on several datasets show that our method can obtain a considerable number of nearly pure correct matches from urban ground and aerial images, significantly outperforming those existing methods.
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title_short	Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images
url	https://doi.org/10.1109/JSTARS.2021.3069222 https://doaj.org/article/57836b6831b6414ba53f976b3b567b73 https://ieeexplore.ieee.org/document/9387538/ https://doaj.org/toc/2151-1535
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In this strategy, the topology structure of repeated façade elements (i.e., lattice), and the high reliable point matching seeds, are first extracted from the rectified ground and aerial images. Then, the point matching seeds guide the self-similar lattice tiles from two views to be precisely aligned, thereby estimating an accurate transformation model from lattice tile correspondences. Finally, the estimated model powerfully supervises the differentiation of true and false matches from the entire putative match set. 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Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images

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