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 function not only worked on a single band image as proved by (Espindola, G.M., Camara, G., Reis, I.A., Bins, L.S. and Monteiro, A.M., 2006, Parameter selection for region-growing image
segmentation algorithms using spatial autocorrelation. International Journal of Remote Sensing, 27, pp. 3035–3040.) but also on multi-spectral imagery as tested in this, and is indeed an effective way to determine the optimal segmentation parameters. McNemar's test (z
2 = 10.27)
shows that with the optimal segmentation, object-based classification achieved accuracy significantly higher than that of the pixel-based classification, with 99% significance level.
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Document Type: Research Article
Centro de Investigaciones en Geografía Ambiental-Universidad Nacional Autónoma de México (UNAM), MoreliaMichoacán, Mexico
International Institute for Geoinformation Science and Earth Observation (ITC), PO Box 6, 7500AA Enschede, The Netherlands
Publication date: July 10, 2011
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