dc.creatorAuquilla Sangolqui, Andrés Vinicio
dc.creatorVanegas Peralta, Pablo Fernando
dc.date.accessioned2018-01-11T21:21:52Z
dc.date.accessioned2022-10-20T22:46:18Z
dc.date.available2018-01-11T21:21:52Z
dc.date.available2022-10-20T22:46:18Z
dc.date.created2018-01-11T21:21:52Z
dc.date.issued2014-06-30
dc.identifier9783319091433
dc.identifier3029743
dc.identifierhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84904895540&doi=10.1007%2f978-3-319-09144-0_25&partnerID=40&md5=8c65e25e306040fa7279ce7bd142e647
dc.identifierhttp://dspace.ucuenca.edu.ec/handle/123456789/22141
dc.identifier10.1007/978-3-319-09144-0_25
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4612688
dc.description.abstractIn an Object Based Image Analysis Classification (OBIA) process, the quality of the classification results are highly dependent on segmentation. However, a high number of the studies that make use of an OBIA process find the segmentation parameters by making use of trial-and-error methods. It is clear that a lack of a structured procedure to determine the segmentation parameters produces unquantified errors in the classification. This paper aims to quantify the effects of using a semi-automatic approach to determine optimal segmentation parameters. To this end, an OBIA process is performed to classify land cover types produced by both a manual and an automatic segmentation. Even though the classification using the manual segmentation outperforms the automatic segmentation, the difference is only 2%. Since the automatic segmentation is performed with optimal parameters, a procedure to accurately determine those parameters must be performed to minimize the error produced by a misjudgment in the segmentation step. © 2014 Springer International Publishing.
dc.languageen_US
dc.publisherSPRINGER VERLAG
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.subjectClassification
dc.subjectComparison Index
dc.subjectObia
dc.subjectSegmentation
dc.subjectSegmentation Parameters
dc.subjectSupport Vector Machines
dc.titleA procedure for semi-automatic segmentation in OBIA based on the maximization of a comparison index
dc.typeArticle


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