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DC Field | Value | Language |
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dc.contributor.author | Auquilla Sangolqui, Andrés Vinicio | - |
dc.contributor.author | Vanegas Peralta, Pablo Fernando | - |
dc.date.accessioned | 2018-01-11T21:21:52Z | - |
dc.date.available | 2018-01-11T21:21:52Z | - |
dc.date.issued | 2014-06-30 | - |
dc.identifier.isbn | 9783319091433 | - |
dc.identifier.issn | 3029743 | - |
dc.identifier.uri | https://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.identifier.uri | http://dspace.ucuenca.edu.ec/handle/123456789/22141 | - |
dc.description.abstract | In 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.language.iso | en_US | - |
dc.publisher | SPRINGER VERLAG | - |
dc.source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.subject | Classification | - |
dc.subject | Comparison Index | - |
dc.subject | Obia | - |
dc.subject | Segmentation | - |
dc.subject | Segmentation Parameters | - |
dc.subject | Support Vector Machines | - |
dc.title | A procedure for semi-automatic segmentation in OBIA based on the maximization of a comparison index | - |
dc.type | Article | - |
dc.description.city | Guimaraes | - |
dc.ucuenca.idautor | 0103557369 | - |
dc.ucuenca.idautor | 0102274891 | - |
dc.identifier.doi | 10.1007/978-3-319-09144-0_25 | - |
dc.ucuenca.embargoend | 2022-01-01 0:00 | - |
dc.ucuenca.afiliacion | auquilla, a., computer science department, universidad de cuenca, cuenca, ecuador, centre for industrial management, department of mechanical engineering, ku leuven, celestijnenlaan 300a, b-3000 leuven, belgium | - |
dc.ucuenca.afiliacion | vanegas, p., computer science department, universidad de cuenca, cuenca, ecuador | - |
dc.ucuenca.volumen | 8579 LNCS | - |
dc.ucuenca.indicebibliografico | SCOPUS | - |
dc.ucuenca.nombrerevista | 14th International Conference on Computational Science and Its Applications ICCSA 2014 | - |
Appears in Collections: | Artículos |
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documento.pdf | 168.92 kB | Adobe PDF | View/Open |
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