A basic problem of image classification in remote sensing is to select suitable image features. However, modern classifiers such as AdaBoost allow for feature selection driven by the training data. This capability brings up the question whether hand-crafted features are required or whether it would not be enough to extract the same quasi-exhaustive feature set for different classification problems and let the classifier choose a suitable subset for the specific image statistics of the given problem. To be able to efficiently extract a large quasi-exhaustive set of multi-scale texture and intensity features we suggest to approximate standard derivative filters via integral images. We compare our <i>quasi-exhaustive features</i> to several standard feature sets on four very high-resolution (VHR) aerial and satellite datasets of urban areas. We show that in combination with a boosting classifier the proposed <i>quasi-exhaustive features</i> outperform standard baselines.