  An image preprocessing methodology based on Fourier analysis together with
the Laguerre-Gauss Spatial Filter is proposed. This is an alternative to obtain
features from aerial images that reduces the feature space significantly,
preserving enough information for classification tasks. Experiments on a
challenging data set of aerial images show that it is possible to learn a
robust classifier from this transformed and smaller feature space using simple
models, with similar performance to the complete feature space and more complex
models.
