Subpixel automatic detection of GCP coordinates in time-lapse images using a deep learning keypoint network

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Authors: Blanch Gorriz, Xabier|||0000-0003-2694-4475, Jäschke, Almut, Elias, Melanie, Eltner, Anette
Format: article
Publication Date:2024
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/422973
Online Access:https://hdl.handle.net/2117/422973
https://dx.doi.org/10.1109/TGRS.2024.3514854
Access Level:Open access
Keyword:Automatic detection
Ground control point (GCP)
Photogrammetry
ResNet50
Àrees temàtiques de la UPC::Enginyeria civil::Geomàtica::Fotogrametria
id ES_a7bebf1f54840e1433a07ab5b1f8fbfa
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repository_id_str
spelling Subpixel automatic detection of GCP coordinates in time-lapse images using a deep learning keypoint networkBlanch Gorriz, Xabier|||0000-0003-2694-4475Jäschke, AlmutElias, MelanieEltner, AnetteAutomatic detectionGround control point (GCP)PhotogrammetryResNet50Àrees temàtiques de la UPC::Enginyeria civil::Geomàtica::Fotogrametria© 2024 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.In this study, we present an approach to exploit the keypoint R-convolutional neural network (CNN) structure for automated detection of ground control points (GCPs) in images acquired by optical sensors. Our deep learning methodology employs fine-tuning on three distinct datasets, thereby enabling the specific addressing of inherent variations in GCP types, including shape, size, and color. Our approach uses an end-to-end artificial intelligence (AI)-based approach that does not require any postprocessing of the data or the use of specific GCPs. Performance metrics are evaluated to compare the AI results with manually labeled data, coordinates obtained by an ellipse fitting algorithm for circular targets, and a semi-automatic optical flow algorithm for arbitrary targets (e.g., crosses). The study highlights the importance of a dataset-specific fine-tuning and data augmentation strategy to enhance the model’s accuracy in locating GCPs. The results demonstrate the effectiveness of the R-CNN keypoint approach in successfully identifying GCPs with subpixel accuracy compared to manual labeling in all datasets. Our AI-based approach outperformed semi-automatic methods based on computer vision algorithms, identifying GCPs in images taken under adverse conditions and in cases where GCPs were partially obscured. Furthermore, the results demonstrate robust performance in a transferability test, that is, identifying GCPs in different images obtained at different locations and with different cameras than those used for training.Peer ReviewedInstitute of Electrical and Electronics Engineers (IEEE)20242024-12-1120252025-01-28journal articlehttp://purl.org/coar/resource_type/c_6501AOhttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/422973https://dx.doi.org/10.1109/TGRS.2024.3514854reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4229732026-05-27T15:37:01Z
dc.title.none.fl_str_mv Subpixel automatic detection of GCP coordinates in time-lapse images using a deep learning keypoint network
title Subpixel automatic detection of GCP coordinates in time-lapse images using a deep learning keypoint network
spellingShingle Subpixel automatic detection of GCP coordinates in time-lapse images using a deep learning keypoint network
Blanch Gorriz, Xabier|||0000-0003-2694-4475
Automatic detection
Ground control point (GCP)
Photogrammetry
ResNet50
Àrees temàtiques de la UPC::Enginyeria civil::Geomàtica::Fotogrametria
title_short Subpixel automatic detection of GCP coordinates in time-lapse images using a deep learning keypoint network
title_full Subpixel automatic detection of GCP coordinates in time-lapse images using a deep learning keypoint network
title_fullStr Subpixel automatic detection of GCP coordinates in time-lapse images using a deep learning keypoint network
title_full_unstemmed Subpixel automatic detection of GCP coordinates in time-lapse images using a deep learning keypoint network
title_sort Subpixel automatic detection of GCP coordinates in time-lapse images using a deep learning keypoint network
dc.creator.none.fl_str_mv Blanch Gorriz, Xabier|||0000-0003-2694-4475
Jäschke, Almut
Elias, Melanie
Eltner, Anette
author Blanch Gorriz, Xabier|||0000-0003-2694-4475
author_facet Blanch Gorriz, Xabier|||0000-0003-2694-4475
Jäschke, Almut
Elias, Melanie
Eltner, Anette
author_role author
author2 Jäschke, Almut
Elias, Melanie
Eltner, Anette
author2_role author
author
author
dc.subject.none.fl_str_mv Automatic detection
Ground control point (GCP)
Photogrammetry
ResNet50
Àrees temàtiques de la UPC::Enginyeria civil::Geomàtica::Fotogrametria
topic Automatic detection
Ground control point (GCP)
Photogrammetry
ResNet50
Àrees temàtiques de la UPC::Enginyeria civil::Geomàtica::Fotogrametria
description © 2024 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.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-12-11
2025
2025-01-28
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AO
http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/422973
https://dx.doi.org/10.1109/TGRS.2024.3514854
url https://hdl.handle.net/2117/422973
https://dx.doi.org/10.1109/TGRS.2024.3514854
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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