Optimal region growing segmentation and its effect on classification accuracy
Por:
Gao Y., Mas J.F., Kerle N., Pacheco, JAN
Publicada:
1 ene 2011
Categoría:
Earth and Planetary Sciences (miscellaneous)
Resumen:
Image segmentation is a preliminary and critical step in object-based image classification. Its proper evaluation ensures that the best segmentation is used in image classification. In this article, image segmentations with nine different parameter settings were carried out with a multi-spectral Landsat imagery and the segmentation results were evaluated with an objective function that aims at maximizing homogeneity within segments and separability between neighbouring segments. The segmented images were classified into eight land-cover classes and the classifications were evaluated with independent ground data comprising 600 randomly distributed points. The accuracy assessment results presented similar distribution as that of the objective function values, that is segmentations with the highest objective function values also resulted in the highest classification accuracies. This result shows that image segmentation has a direct effect on the classification accuracy; the objective fun
Filiaciones:
Gao Y.:
Univ Nacl Autonoma Mexico, Ctr Invest Geog Ambiental, Morelia, Michoacan, Mexico
Mas J.F.:
Univ Nacl Autonoma Mexico, Ctr Invest Geog Ambiental, Morelia, Michoacan, Mexico
Kerle N.:
International Institute for Geoinformation Science and Earth Observation (ITC), PO Box 6, 7500 AA Enschede, Netherlands
Pacheco, JAN:
Univ Nacl Autonoma Mexico, Ctr Invest Geog Ambiental, Morelia, Michoacan, Mexico
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