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Incorporating Spatial Variability Measures in Land-cover Classification using Random Forest

Opened Access Incorporating Spatial Variability Measures in Land-cover Classification using Random Forest

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Autor: Rodríguez Galiano, Víctor Francisco
Abarca Hernández, Francisco
Ghimire, Bardan
Chica Olmo, M.
Atkinson, Peter
Jeganathan, Chockalingam
Departamento: Universidad de Sevilla. Departamento de Geografía Física y Análisis Geográfico Regional
Fecha: 2011
Publicado en: Procedia Environmental Sciences, 3, 44-49.
Tipo de documento: Artículo
Resumen: The spatial variability of remotely sensed image values provides important information about the arrangement of objects and their spatial relationships within the image. The characterisation of spatial variability in such images, for example, to measure of texture, is of great utility for the discrimination of land cover classes. To this end, the variogram, a function commonly applied in geostatistics, has been used widely to extract image texture for remotely sensed data classification. The aim of this study was to assess the increase in accuracy that can be achieved by incorporating univariate and multivariate textural measures of Landsat TM imagery in classification models applied to large heterogeneous landscapes. Such landscapes which difficult to classify due to the large number of land cover categories and low inter-class separability. Madogram, rodogram and direct variogram for the univariate case, and cross- and pseudocross variograms for the multivariate one, together wit...
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Cita: Rodríguez Galiano, V.F., Abarca Hernández, F., Ghimire, B., Chica Olmo, M., Atkinson, P. y Jeganathan, C. (2011). Incorporating Spatial Variability Measures in Land-cover Classification using Random Forest. Procedia Environmental Sciences, 3, 44-49.
Tamaño: 149.9Kb
Formato: PDF

URI: https://hdl.handle.net/11441/74050

DOI: 10.1016/j.proenv.2011.02.009

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