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Title: A procedure for semi-automatic segmentation in OBIA based on the maximization of a comparison index
Authors: Auquilla Sangolqui, Andrés Vinicio
Vanegas Peralta, Pablo Fernando
Keywords: Classification
Comparison Index
Segmentation Parameters
Support Vector Machines
Issue Date: 30-Jun-2014
metadata.dc.ucuenca.embargoend: 1-Jan-2022
metadata.dc.ucuenca.volumen: 8579 LNCS
metadata.dc.source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
metadata.dc.identifier.doi: 10.1007/978-3-319-09144-0_25
metadata.dc.type: Article
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.
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
ISBN: 9783319091433
ISSN: 3029743
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