Segmentation of aerial images for plausible detail synthesis

The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories disting...

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Detalles Bibliográficos
Autores: Argudo Medrano, Óscar|||0000-0003-3943-1839, Comino Trinidad, Marc|||0000-0001-5621-7565, Chica Calaf, Antonio|||0000-0003-0270-2332, Andújar Gran, Carlos Antonio|||0000-0002-8480-4713, Lumbreras, Felipe
Tipo de recurso: capítulo de libro
Fecha de publicación:2018
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/115550
Acceso en línea:https://hdl.handle.net/2117/115550
https://dx.doi.org/10.1016/j.cag.2017.11.004
Access Level:acceso abierto
Palabra clave:Image segmentation
Terrain editing
Detail synthesis
Vegetation synthesis
Terrain rendering
Imatges -- Segmentació
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
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oai_identifier_str oai:upcommons.upc.edu:2117/115550
network_acronym_str ES
network_name_str España
repository_id_str
spelling Segmentation of aerial images for plausible detail synthesisArgudo Medrano, Óscar|||0000-0003-3943-1839Comino Trinidad, Marc|||0000-0001-5621-7565Chica Calaf, Antonio|||0000-0003-0270-2332Andújar Gran, Carlos Antonio|||0000-0002-8480-4713Lumbreras, FelipeImage segmentationTerrain editingDetail synthesisVegetation synthesisTerrain renderingImage segmentationImatges -- SegmentacióÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàticaThe visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts.Peer Reviewed20182018-04-0120182018-03-22book parthttp://purl.org/coar/resource_type/c_3248AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/bookPartapplication/pdfhttps://hdl.handle.net/2117/115550https://dx.doi.org/10.1016/j.cag.2017.11.004reponame: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:2117/1155502026-05-27T15:37:01Z
dc.title.none.fl_str_mv Segmentation of aerial images for plausible detail synthesis
title Segmentation of aerial images for plausible detail synthesis
spellingShingle Segmentation of aerial images for plausible detail synthesis
Argudo Medrano, Óscar|||0000-0003-3943-1839
Image segmentation
Terrain editing
Detail synthesis
Vegetation synthesis
Terrain rendering
Image segmentation
Imatges -- Segmentació
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
title_short Segmentation of aerial images for plausible detail synthesis
title_full Segmentation of aerial images for plausible detail synthesis
title_fullStr Segmentation of aerial images for plausible detail synthesis
title_full_unstemmed Segmentation of aerial images for plausible detail synthesis
title_sort Segmentation of aerial images for plausible detail synthesis
dc.creator.none.fl_str_mv Argudo Medrano, Óscar|||0000-0003-3943-1839
Comino Trinidad, Marc|||0000-0001-5621-7565
Chica Calaf, Antonio|||0000-0003-0270-2332
Andújar Gran, Carlos Antonio|||0000-0002-8480-4713
Lumbreras, Felipe
author Argudo Medrano, Óscar|||0000-0003-3943-1839
author_facet Argudo Medrano, Óscar|||0000-0003-3943-1839
Comino Trinidad, Marc|||0000-0001-5621-7565
Chica Calaf, Antonio|||0000-0003-0270-2332
Andújar Gran, Carlos Antonio|||0000-0002-8480-4713
Lumbreras, Felipe
author_role author
author2 Comino Trinidad, Marc|||0000-0001-5621-7565
Chica Calaf, Antonio|||0000-0003-0270-2332
Andújar Gran, Carlos Antonio|||0000-0002-8480-4713
Lumbreras, Felipe
author2_role author
author
author
author
dc.subject.none.fl_str_mv Image segmentation
Terrain editing
Detail synthesis
Vegetation synthesis
Terrain rendering
Image segmentation
Imatges -- Segmentació
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
topic Image segmentation
Terrain editing
Detail synthesis
Vegetation synthesis
Terrain rendering
Image segmentation
Imatges -- Segmentació
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
description The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-04-01
2018
2018-03-22
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/115550
https://dx.doi.org/10.1016/j.cag.2017.11.004
url https://hdl.handle.net/2117/115550
https://dx.doi.org/10.1016/j.cag.2017.11.004
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.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 15,301603

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