Please use this identifier to cite or link to this item: https://hdl.handle.net/10953/1723
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dc.contributor.authorRuiz-Lendínez, Juan J.-
dc.contributor.authorUreña-Cámara, Manuel A.-
dc.contributor.authorMesa-Mingorance, José L.-
dc.contributor.authorQuesada-Real, Francisco J.-
dc.date.accessioned2024-01-29T08:44:06Z-
dc.date.available2024-01-29T08:44:06Z-
dc.date.issued2021-
dc.identifier.issn2220-9964es_ES
dc.identifier.otherhttps://doi.org/10.3390/ijgi10070430es_ES
dc.identifier.urihttps://www.mdpi.com/2220-9964/10/7/430es_ES
dc.identifier.urihttps://hdl.handle.net/10953/1723-
dc.descriptionUtilizar bajo licencia CC BY 4.0es_ES
dc.description.abstractThere 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.es_ES
dc.description.sponsorshipPID2019-106195RB-100 (Ministerio de Ciencia, Innovación y Universidades).es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.ispartofISPRS International Journal of Geographical Information Science 2021; 10(7): 430es_ES
dc.rightsAtribución 3.0 España*
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectautomationes_ES
dc.subjectpositional accuracy assessmentes_ES
dc.subjecttextural imagery segmentationes_ES
dc.subjectdiscrepancy methodses_ES
dc.titleAutomatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Studyes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
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