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Roof detection on satellite images

In digital image classification processes, image pixels are separated according to some features that they have. Satellite images are digital images that are taken by a satellite vehicle through some sensors which perceive the specific wavelength of the light. In this study, two different digital image classification method (Linear Discriminant Analysis and Normalized Distance Values) have compared to each other, using different color spaces (RGB, L∗a∗b∗ and HSV), on the satellite images that have been taken by digital airborne sensors so as to detect roof objects. The common features of the applied methods are those they are supervised because of using training data given previously and they run fast because of operating linearly using a threshold value. For this reason, some of the images in the dataset are used for the purpose of training in order to detect the certain coefficients and the threshold value. The dataset we used for training and testing are the images acquired from ISPRS WG III/4 2D Semantic Labeling database. In the database, the classification ground truth images are also available.