A procedure for semi-automatic segmentation in OBIA based on the maximization of a comparison index
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 cle...
| Autores: | , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | Ecuador |
| Recursos: | Universidad de Cuenca |
| Repositorio: | Repositorio Universidad de Cuenca |
| OAI Identifier: | oai:dspace.ucuenca.edu.ec:123456789/22141 |
| Acesso em linha: | 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 http://dspace.ucuenca.edu.ec/handle/123456789/22141 |
| Access Level: | acceso abierto |
| Palavra-chave: | Classification Comparison Index Obia Segmentation Segmentation Parameters Support Vector Machines |
| Resumo: | 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. |
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