Segmentation and decision fusion for building detection

Segment based classification is one of the popular approaches for object detection, where the performance of the classification task is sensitive to the accuracy of the output of the initial segmentation. Most of these studies includes generic segmentation methods and it is assumed that the segmentation output is compatible with the subsequent classification method. However, depending on the problem domain the properties of the regions such as size, shape etc. which are suitable for classification may vary. In this study, we propose a domain specific segmentation method for building detection. The contribution of the domain specific segmentation is empirically analyzed. For this purpose, first the decision fusion method is employed for building detection on the outputs of the state of the art segmentation methods, then it is employed on the output of the domain specific segmentation method and the classification performances for each method are compared. The advantage of domain specific segmentation is observed quantitatively and satisfactory results are obtained.