## 1 Introduction  
Extraction of information on the built environment from remote sensing imagery is a complex task, mainly due to the manifold combinations of surface materials and the diversity of size, shape and placement of the objects composing a typical image scene. It is increasingly being recognized that image domains beyond spectral information, such as geometrical, temporal or textural domains must be utilized in order to tackle the complexity of the information extraction. In this regard, algorithms that make use of the extended information content of image segments (also referred to as super-pixels or objects) have been adapted by the remote sensing community in recent years [1]. Such an object-based image analysis emerged primarily in the context of Very High Resolution (VHR) image analysis (Ground Sampling Distance (GSD) \(<\) 1-4 m), where it showed advantages over pixel-based approaches [2] for extracting detailed thematic information such as single buildings or streets [3,4]. An object-based image analysis can also be beneficial, when analyzing the built environment from Medium Resolution (MR) satellite images (GSD 15-60 m) [5,6], where different urban patterns are mainly defined by the placement of buildings, streets and open-spaces and therefore cannot sufficiently be described by just the spectral values of a single pixel. When segments are used as the basic spatial entity for analysis, a large number of image features can be generated to describe the objects of interest in an image. This includes spectral features such as mean and standard deviation values per image band, minimum and maximum pixel values, mean band ratios or mean and standard deviation of band indices such as the Normalized Difference Vegetation Index (NDVI) [7]. Textural features, which take into account neighborhood relations between pixels, can be effectively computed over the segments. Commonly used textural descriptors in remote sensing analysis include second-order texture features derived from the Gray-Level Co-occurrence Matrix (GLCM), such as angular second moment, entropy, contrast or correlation. A mathematical notation of the GLCM and the related second-order textural features is given in [PERSON] _et al._[8]. A class of features unique to object-based approaches that has frequently claimed to provide major improvements in object recognition is geometrical features [1,9]. Geometrical features or shape descriptors can be generally divided into contour-based and region-based. Contour-based features are extracted from the contour of an object and include, amongst others, convexity, perimeter, compactness or major axis orientation. Region-based features are extracted from the shape region and include area, eccentricity or moment features like the Hu moments [10]. Reviews of shape representation and description techniques with mathematical explanations of the different mentioned descriptors can be found in [11,12]. To reduce the dimensionality of the feature space and preselect the most significant features for a specific classification task, dataset and classifier, several feature selection methods have been proposed in the literature [13,14]. The most commonly found method in remote sensing applications is a manual feature selection with conventional data exploration tools such as histograms or scatter-plots. This method requires a sound understanding of the classification task and the characteristics of the features under observation. However, increasing the number of features of manual methods becomes impracticable and more quantitative feature selection techniques are needed [15]. The ReliefF feature ranking algorithm for example has been widely used in other research fields such as medical imaging, and has been successfully applied to noisy and correlated datasets [14]. Despite its great potential, it has only rarely been used in remote sensing applications so far [16].