## 1 Introduction
To partition a multi-dimensional feature space into homogeneous areas and therefore to label segments with respect to desired classes, several image classification algorithms are available, commonly categorized into parametric and non-parametric classifiers. Parametric classifiers assume a normally distributed dataset and statistical parameters are directly inferred from training data [4]. A parametric classifier, which has been widely used in remote sensing application, is the Normal Bayes classifier. Non-parametric classifiers do not assume a specific data distribution to separate a multi-dimensional feature space into classes. Most commonly used non-parametric classifiers include Decision Trees, Support Vector Machines and Expert Systems. It can be inferred from the literature that machine learning algorithms (both parametric and non-parametric) have mainly been used as per-pixel classifiers in remote sensing image analysis [4]. For the classification of image segments, many studies so far have used fuzzy set theory-based expert systems [17,18]. Expert systems are considered to have a great potential for image classification when also considering ancillary data [4]. However, a critical part of such systems is the development of rule sets to describe the classes of interest. Often threshold values need to be manually adjusted and classification rules are strongly bound to a particular image scene and image type. Only a few studies have designed generalized rule sets that aim at being transferable [3]. The application of machine learning algorithms in the context of an object-based image analysis has only rarely been tested [19] and further research in this direction is advocated. In particular, the potential of applying pre-trained learning machines to different image types and image scenes should be further assessed. While most pattern recognition methods in remote sensing are trained and tested on the same image distribution, real-world applications often involve changing visual domains (e.g., multi-temporal change detection, regional landuse/landcover mapping). In general, satellite image distributions could differ in combinations of partially uncontrollable or unknown factors, including atmospheric effects, sensor gain or seasonal variations in the image scene. Recent studies have proposed the use of domain adaptation [20], retraining [21] or multi-task learning [22] techniques to improve the transferability of pixel-based classifications in the spectral domain. The question of transferability of classifiers on an extended and partly domain-invariant feature space derived from an object-based image analysis remains still largely unanswered in remote sensing applications. The objectives of this study are to develop a method and the related software tools based on Free and Open-Source Solutions (FOSS) for the recognition of urban patterns in multi-spectral satellite images. Four machine learning classification algorithms (Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines) are implemented and thoroughly tested for their performance under varying conditions to optimize their usage and assess their applicability in the context of an object-based image analysis. In particular, the following research questions are addressed:  
1. What are the most important image features for class separability? 2. How sensitive are learning machines to the size of the feature vector? 3. How does the number of training instances influence the performance of the learning machines? 4. How well can a trained learning machine be transferred between different image types and image scenes? 5. How does image segmentation influence the classification results?The goal is to develop classifiers for the recognition of built-up areas from different MR and VHR image types that are able to maximize true positive and true negative detection rates, while minimizing the number of input features and training instances to reduce computation time and training efforts.