Abstract: In the last three decades, advances in computer technology, earth observation sensors and GIS science, led to the development of “Object-based Image Analysis” as an alternative to the traditional pixel-based image analysis. Many studies have shown that traditional pixel-based image analysis is limited because it uses only spectral information of single pixels and this approach produces poor results especially with high spatial resolution satellite images. By contrast, object-based image analysis works on (homogeneous) objects which are produced by image segmentation and allows using more elements in the classification. As an object is a group of pixels, object characteristics such as mean value, standard deviation of spectral values, etc. can be calculated; besides shape and texture features of the objects are available and can be used to differentiate land cover classes with similar spectral information. These extra types of information give object-based image analysis the potential to produce land cover thematic maps with higher accuracies than those produced by traditional pixel-based method. In this study, we look at the performance of object-based image analysis in classifying satellite images with different spatial resolutions; comparing the classification results with those produced by the pixel-based method, we intend to find out how spatial resolution of satellite images influences the performance of object-based image analysis. The experiment showed that with relatively high spatial resolution images such as SPOT-5 and Landsat-7 ETM+, object-based image analysis obtained higher accuracy than that by the pixel-based one; while for coarse resolution images with 100 and 250 m spatial resolution, object based image analysis did not obtain higher accuracy. This study shows that the object-based image analysis has advantage over the pixel-based one, however the advantage was only found in the higher spatial resolution images.