Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching.

Shi, Y; Yu, X; Liu, L; Campbell, D; Koniusz, P; Li, H

Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching.

Shi, Y Yu, X

Liu, L Campbell, D Koniusz, P Li, H

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Trans Pattern Anal Mach Intell, 2022, PP, (3), pp. 2682-2697
Issue Date:: 2022-07-11

Closed Access

	Filename	Description	Size
	Accurate_3-DoF_Camera_Geo-Localization_via_Ground-to-Satellite_Image_Matching.pdf	Published version	6.16 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Shi, Y
dc.contributor.author	Yu, X https://orcid.org/0000-0002-0269-5649
dc.contributor.author	Liu, L
dc.contributor.author	Campbell, D
dc.contributor.author	Koniusz, P
dc.contributor.author	Li, H
dc.date.accessioned	2023-03-19T23:59:21Z
dc.date.available	2023-03-19T23:59:21Z
dc.date.issued	2022-07-11
dc.identifier.citation	IEEE Trans Pattern Anal Mach Intell, 2022, PP, (3), pp. 2682-2697
dc.identifier.issn	0162-8828
dc.identifier.issn	1939-3539
dc.identifier.uri	http://hdl.handle.net/10453/167638
dc.description.abstract	We address the problem of ground-to-satellite image geo-localization, that is, estimating the camera latitude, longitude and orientation (azimuth angle) by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our prior arts treat the above task as pure image retrieval by selecting the most similar satellite reference image matching the ground-level query image. However, such an approach often produces coarse location estimates because the geotag of the retrieved satellite image only corresponds to the image center while the ground camera can be located at any point within the image. To further consolidate our prior research finding, we present a novel geometry-aware geo-localization method. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image, once its coarse location and orientation have been determined. Moreover, we propose a new geometry-aware image retrieval pipeline to improve the coarse localization accuracy. Apart from a polar transform in our conference work, this new pipeline also maps satellite image pixels to the ground-level plane in the ground-view via a geometry-constrained projective transform to emphasize informative regions, such as road structures, for cross-view geo-localization. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our newly proposed framework. We also significantly improve the performance of coarse localization results compared to the state-of-the-art in terms of location recalls.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP220100800
dc.relation.ispartof	IEEE Trans Pattern Anal Mach Intell
dc.relation.isbasedon	10.1109/TPAMI.2022.3189702
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching.
dc.type	Journal Article
utslib.citation.volume	PP
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic 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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-19T23:59:20Z
pubs.issue	3
pubs.publication-status	Published online
pubs.volume	PP
utslib.citation.issue	3

Abstract:

We address the problem of ground-to-satellite image geo-localization, that is, estimating the camera latitude, longitude and orientation (azimuth angle) by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our prior arts treat the above task as pure image retrieval by selecting the most similar satellite reference image matching the ground-level query image. However, such an approach often produces coarse location estimates because the geotag of the retrieved satellite image only corresponds to the image center while the ground camera can be located at any point within the image. To further consolidate our prior research finding, we present a novel geometry-aware geo-localization method. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image, once its coarse location and orientation have been determined. Moreover, we propose a new geometry-aware image retrieval pipeline to improve the coarse localization accuracy. Apart from a polar transform in our conference work, this new pipeline also maps satellite image pixels to the ground-level plane in the ground-view via a geometry-constrained projective transform to emphasize informative regions, such as road structures, for cross-view geo-localization. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our newly proposed framework. We also significantly improve the performance of coarse localization results compared to the state-of-the-art in terms of location recalls.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/167638