Meter to sub-meter resolution satellite images have generated new interests in extracting man-made structures in the urban area. However, classification accuracies for such purposes are far from satisfactory. Spectral characteristics of urban land cover classes are so similar that they cannot be separated using only spectral information. As a result, there is an increased interest in incorporating geometrical information. In current literature, this is achieved by using an object-based approach. This requires a segmentation process. However, the complex objects in urban remote sensing images make this process very difficult. In this paper, we propose a method to measure the minimum and maximum dimension of an object, without however performing a segmentation. This method is based on morphological profiles (MP). Previous work on MP's have shown the potential for improving classification results. However, an MP contains many values for each pixel, which can lead to problems of dimensionality. Feature extraction algorithms could reduce the dimensionality, but the resulting features are no longer interpretable. In this paper we use MP's to derive a measure of minimum and maximum object dimension. These two measures allow to differentiate between long (roads) and more compact objects (buildings). We show that these new features improve the classification substantially.