Building detection using local features and DSM data

Detecting and locating buildings in satellite images has various application areas. Unfortunately, manually detecting buildings is hard and very time consuming. Therefore, in the literature several methods are proposed to automatically detect buildings. These methods can be divided into two main groups. In the first group, researchers used panchromatic or multispectral information to detect buildings. In the second group, researchers used DSM data to detect buildings. In this study, we propose two novel methods to detect buildings by combining the panchromatic and DSM data. The first method uses corner points extracted by Harris corner detection method. These corner points are used jointly with DSM data. Using a kernel based density estimation method, possible building locations are detected. In the second method, shadow of buildings are used in a similar way. We tested both methods on WorldView-2 images and DSM data generated from them.