Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study

There are many studies related to Imagery Segmentation (IS) in the field of Geographic Information (GI). However, none of them address the assessment of IS results from a positional perspective. In a field in which the positional aspect is critical, it seems reasonable to think that the quality asso...

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Autores: Ruiz Lendínez, Juan José, Ureña Cámara, Manuel Antonio, Mesa Mingorance, José Luis, Quesada Real, Francisco José
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/4244
Acceso en línea:https://www.mdpi.com/2220-9964/10/7/430
https://hdl.handle.net/10953/4244
Access Level:acceso abierto
Palabra clave:automation
positional accuracy assessment
textural imagery segmentation
discrepancy methods
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spelling Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case StudyRuiz Lendínez, Juan JoséUreña Cámara, Manuel AntonioMesa Mingorance, José LuisQuesada Real, Francisco Joséautomationpositional accuracy assessmenttextural imagery segmentationdiscrepancy methodsThere are many studies related to Imagery Segmentation (IS) in the field of Geographic Information (GI). However, none of them address the assessment of IS results from a positional perspective. In a field in which the positional aspect is critical, it seems reasonable to think that the quality associated with this aspect must be controlled. This paper presents an automatic positional accuracy assessment (PAA) method for assessing this quality component of the regions obtained by means of the application of a textural segmentation algorithm to a Very High Resolution (VHR) aerial image. This method is based on the comparison between the ideal segmentation and the computed segmentation by counting their differences. Therefore, it has the same conceptual principles as the automatic procedures used in the evaluation of the GI’s positional accuracy. As in any PAA method, there are two key aspects related to the sample that were addressed: (i) its size—specifically, its influence on the uncertainty of the estimated accuracy values—and (ii) its categorization. Although the results obtained must be taken with caution, they made it clear that automatic PAA procedures, which are mainly applied to carry out the positional quality assessment of cartography, are valid for assessing the positional accuracy reached using other types of processes. Such is the case of the IS process presented in this study.MDPI202520252021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.mdpi.com/2220-9964/10/7/430https://hdl.handle.net/10953/4244reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésISPRS. International Journal o f Geo-Informationinfo:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/42442026-06-24T12:41:07Z
dc.title.none.fl_str_mv Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study
title Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study
spellingShingle Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study
Ruiz Lendínez, Juan José
automation
positional accuracy assessment
textural imagery segmentation
discrepancy methods
title_short Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study
title_full Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study
title_fullStr Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study
title_full_unstemmed Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study
title_sort Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study
dc.creator.none.fl_str_mv Ruiz Lendínez, Juan José
Ureña Cámara, Manuel Antonio
Mesa Mingorance, José Luis
Quesada Real, Francisco José
author Ruiz Lendínez, Juan José
author_facet Ruiz Lendínez, Juan José
Ureña Cámara, Manuel Antonio
Mesa Mingorance, José Luis
Quesada Real, Francisco José
author_role author
author2 Ureña Cámara, Manuel Antonio
Mesa Mingorance, José Luis
Quesada Real, Francisco José
author2_role author
author
author
dc.subject.none.fl_str_mv automation
positional accuracy assessment
textural imagery segmentation
discrepancy methods
topic automation
positional accuracy assessment
textural imagery segmentation
discrepancy methods
description There are many studies related to Imagery Segmentation (IS) in the field of Geographic Information (GI). However, none of them address the assessment of IS results from a positional perspective. In a field in which the positional aspect is critical, it seems reasonable to think that the quality associated with this aspect must be controlled. This paper presents an automatic positional accuracy assessment (PAA) method for assessing this quality component of the regions obtained by means of the application of a textural segmentation algorithm to a Very High Resolution (VHR) aerial image. This method is based on the comparison between the ideal segmentation and the computed segmentation by counting their differences. Therefore, it has the same conceptual principles as the automatic procedures used in the evaluation of the GI’s positional accuracy. As in any PAA method, there are two key aspects related to the sample that were addressed: (i) its size—specifically, its influence on the uncertainty of the estimated accuracy values—and (ii) its categorization. Although the results obtained must be taken with caution, they made it clear that automatic PAA procedures, which are mainly applied to carry out the positional quality assessment of cartography, are valid for assessing the positional accuracy reached using other types of processes. Such is the case of the IS process presented in this study.
publishDate 2021
dc.date.none.fl_str_mv 2021
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.mdpi.com/2220-9964/10/7/430
https://hdl.handle.net/10953/4244
url https://www.mdpi.com/2220-9964/10/7/430
https://hdl.handle.net/10953/4244
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv ISPRS. International Journal o f Geo-Information
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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