Comparison of pixel-based and object-based classifications of high resolution satellite data in urban fringe areas

This study compares pixel-based and object-based classification of land cover using high resolution satellite data available for urban fringe regions. In the pixel-based analysis the maximum likelihood method and the ISODATA method were applied. The results showed that in both methods misclassification tended to increase due to shadows. The pixel-based classification also experienced difficulty due to factors such as the varied shapes of the forest canopy and mixing of vegetation, etc. The object-based classification, in contrast, relies on abstraction of comparatively homogenous areas, and proved capable of extracting the boundaries among all the forest types. In addition, this study employed a high number of minute patches, and proved effective even in regions where tree species were mingled together. Some misclassification problems remained, which have to be addressed by future trial and error experiments in parameter setting. Still, object-based classification of high resolution satellite data was shown to be an effective tool for analyzing vegetation cover in semi-urbanized and countryside landscapes on the outskirts of large cities, where various vegetation types, as well as buildings and other infrastructures, are mixed together in small areas.