dc.creator | Auquilla Sangolqui, Andrés Vinicio | |
dc.creator | Vanegas Peralta, Pablo Fernando | |
dc.date.accessioned | 2018-01-11T21:21:52Z | |
dc.date.accessioned | 2022-10-20T22:46:18Z | |
dc.date.available | 2018-01-11T21:21:52Z | |
dc.date.available | 2022-10-20T22:46:18Z | |
dc.date.created | 2018-01-11T21:21:52Z | |
dc.date.issued | 2014-06-30 | |
dc.identifier | 9783319091433 | |
dc.identifier | 3029743 | |
dc.identifier | 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 | http://dspace.ucuenca.edu.ec/handle/123456789/22141 | |
dc.identifier | 10.1007/978-3-319-09144-0_25 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4612688 | |
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 | 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 | |