Comparison of Pixel-based and Object-oriented Classification Methods for Extracting Built-up Areas in Aridzone

This study focuses on the comparison between the classical and object-oriented image classifications of remote sensing imagery in the arid area. Due to its special geographic environment and socio-economic contexts, the land cover and its spatio-temporal pattern in aridzone is very different from those in coastal area, thus some conventional methods of remote sensing image classification may not be suitable. In order to investigate an appropriate method for aridzone image classification, pixel-based image classifiers such as the Maximum Likelihood Classifier and an object-oriented image classifier were tested and compared using an Landsat ETM+ image. The accuracy of each method was assessed using reference data sets derived from high-resolution satellite images, aerial photograph and field investigation. The result shows that the object-oriented method has achieved an overall accuracy of 89% with a kappa coefficient of 0.87, compared with 71% (0.66) that was derived from the conventional pixel-based method.