Building Detection and Recognition from High Resolution Remotely Sensed Imagery

This paper supposes a schema to deal with the tough task of building detection and recognition from high resolution remotely sensed imagery. It is a region-based and semi-automatic schema combining with Hough transform and computation of convex hull of the pixels contained in the building areas, which can produce a precise result when the contrast between flat building rooftop and the background is high enough. The first step of this strategy is applying seed region grow algorithm to collect pixels contained in the building region to form the approximation shape of building. In order to retrieve the precise shape of building, we devise two approaches, which are based on Hough transform and convex hull computation, to deal with different scenes. Based on the fact that most buildings in real world can be represented by a convex polygon, the first schema uses this idea to compute the shape of the building. The second schema search the desired shape represented by a related orthogonal corner from the node matrix constructed by the dominate line sets of the building. Extraction result shows this schema supposed is robust and applicable to most high resolution remotely sensed imagery. * Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.