## 6 Discussion  
An extended set of spectral, textural and geometrical features has been implemented and tested within this study for the task of urban pattern recognition from multi-spectral satellite images. For the tested scenarios, spectral and textural features showed the highest importance when distinguishing \"built-up areas\" from \"not built-up areas\". Geometrical features deployed a low importance for class separability for the analyzed image types and classification task (Figure 2). For MR image analysis, this was to be expected due to the spatial resolution, whereas for VHR image analysis the result is in contrast to other studies [1]. A possible explanation could be that distinct rectangular shaped building footprints are often partly covered by vegetation in the satellite image and can therefore not adequately be described by geometrical features such as convexity or compactness. However, this should be further investigated with focus on the possible influence of the segmentation and the influence of the specific scene properties. Moreover, the feature set is not exhaustive and implementation of additional features may further constrain the results. NDVI and the mean spectral values of band 4 (NIR) calculated over the segments proved to be important features for all the image types. Particularly interesting is also the importance of TM band 6 (thermal IR) for class separability. As stated in [PERSON] [45], it has often been ignored in (pixel-based) landcover classification applications, mainly because of its low geometrical resolution (120 m GSD). Given the observation that the average segment size for built-up area recognition in MR satellite images is generally larger than 120 m \(\times\) 120 m,  
Figure 16: Influence of the segmentation inputs on the classification accuracy for MSS with increasing average segment size from left to right. Mean and range of accuracy measures from the ten-fold cross-validation are shown for each iteration. the lower GSD of the thermal IR band did not negatively influence the geometric outline of the objects of interest, but added additional spectral content when included in the classification of the segments. For all classifiers and on all image types, an increase in the accuracies could be observed when the number of training instances was increased until an optimal operating point is reached (Figures 8-10). A further increase of the training dataset size beyond this operating point does not further improve classification accuracy, but stabilizes it. The optimal operating point, defining the minimum number of training instances needed to train a powerful classifier, varied significantly between the different classifiers and compared with the average number of input features for the classifiers, the best ratio of the number of training instances to features was achieved by RT (3.3) and SVM (3.5). KNN (7.1) and NB (14.7) required significantly more training instances with respect to the size of the feature vector to achieve similar classification accuracies. A possible explanation for why SVM is not strongly affected by changes in the number of training instances and the dimensionality of the feature space, could be that it exploits the concept of margin maximization for classification where adding more training instances may not significantly change the composition of support vectors to span the separating hyperplane. In addition, SVM does not require an estimation of the statistical distribution of classes to perform a classification, as is the case for NB. For standard statistical classifiers like NB, an increase in the dimensionality of the feature space increases the difficulty to estimate from a limited number of training instances the multi-dimensional probability density functions. Therefore, NB is strongly affected by the size of the feature vector and the training dataset.