Object-based land use/cover extraction from QuickBird image using Decision tree


By Eltahir Mohamed Elhad, Nagi Zomraw.


The traditional pixel-wise statistical and mono-scale based classification approaches do not lead to satisfactory results for neglecting the shape and context aspects of the image information, which are among the main clues for information extraction at very-high spatial resolutions like QuickBird image. This paper extracts land use/cover information from occurrence filters texture features that were derived from the grey-level occurrence matrix from QuickBird image using CART Decision tree, because, this method have substantial advantages for remote sensing classification problems due to their nonparametric nature, simplicity, robustness with respect to non-linear and noisy relations among input features and class labels, and their computational efficiency. CART has a simple form which can be compactly stored and that efficiently classifies new data ,also it can recursively partitions a data set into smaller subdivisions on the basis of tests applied to one or more features at each node of the tree. Overall accuracy of texture features using CART Decision tree is higher than other methods. It concluded that texture features can be used to improve classification accuracy. [Journal of American Science 2010;6(2):176-180]. (ISSN: 1545-1003


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