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Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/29272
Title: A local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery
Authors: Canovas Garcia, Fulgencio Jose
metadata.dc.ucuenca.correspondencia: Cánovas-García, F.; Departamento de Ingeniería Civil, Universidad de CuencaEcuador
Keywords: Ecognition<Sup>Tm</Sup>
Object-Based Image Analysis
Issue Date: 14-Sep-2015
metadata.dc.ucuenca.embargoend: 1-Jan-2022
metadata.dc.ucuenca.volumen: 30
metadata.dc.source: Geocarto International
metadata.dc.identifier.doi: 10.1080/10106049.2015.1004131
metadata.dc.type: Article
The results obtained using the object-based image analysis approach for remote sensing image analysis depend strongly on the quality of the segmentation step. In this paper, to optimize the scale parameter in a multiresolution segmentation, we analyse a high-resolution image of a large and heterogeneous agricultural area. This approach is based on using a set of agricultural plots extracted from official maps as uniform spatial units. The scale parameter is then optimized in each uniform spatial unit. Intra-object and inter-object heterogeneity measurements are used to evaluate each segmentation. To avoid subsegmentation, some oversegmentation is allowed, but is attenuated in a second step using the spectral difference segmentation algorithm. The statistical distribution of the scale parameter is not equal in all land uses, indicating the soundness of this local approach. A quantitative assessment of the results was also conducted for the different land covers. The results indicate that the spectral contrast between objects is larger with the local approach than with the global approach. These differences were statistically significant in all land uses except irrigated fruit trees and greenhouses. In the absence of subsegmentation, this suggests that the objects will be placed far apart in the space of variables, even if they are very close in the physical space. This is an obvious advantage in a subsequent classification of the objects.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938420549&doi=10.1080%2f10106049.2015.1004131&partnerID=40&md5=40d009484a5dd340faa73b3632a23fdc
ISSN: 10106049
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