Spatial information pertaining to the neighborhood mapping can only be available at the scales of centimeters to meters. In this study, we utilize spatial, structural and contextural features calculated at multiple spatial scales to assess feature-based urban mapping using two different high-spatial resolution sensors (SPOT versus Quickbird) and evaluate the difference in describing neighborhood at these scales. We compared the features that include PanTex, Histogram of Oriented Gradients, Line Support Regions, Hough transformations and others on two selected images that were orthorectified and coregistered. We ran a classification based on the stacks of features for each of the two panchromatic high-spatial resolution images. Our results showed that without using vegetation index as an input, SPOT and Quickbird accomplished comparable classification results, while adding NDVI in Quickbird classification, SPOT image alone does not accomplish comparable classification accuracy using Quickbird image mainly due to lack of vegetation information represented by Normalized Difference Vegetation Index (NDVI), which correlates substantial difference among neighborhoods. The future work can incorporate other multi-spectral information into classification while using SPOT imagery.