Automatic Buildind Detection from High Resolution Images Based on Multiple Features

Automatic recognition and reconstruction of buildings from aerial and space images is of great practical interest for many of applications such as cartography and photo-interpretation. Building detection is the first and very difficult step in building recognition and reconstruction. It is to find buildings and separating them from the background in the presence of distractions caused by other features such as surface markings, vegetation, shadows and highlights. This is an instance of the well-known figure-ground problem. The goal of automatic building detection in this paper is to roughly delineate the rooftop of the buildings that will be verified during the recognition and reconstruction phase. The rooftop detection algorithm proposed here is based on multiple features and proceeds in two steps: first, low-level feature extraction; second, rooftop identification. In this paper we focus on rectangle building roof recognition. In this case, the boundaries of their rooftop are straight lines. One of the obvious facts is that most build roofs are built of materials of limit categories, so their image colors and textures are of limited categories. Low-level features used here are straight-line segments, image colors and image textures. In local edge detection, the vital phase of low-level feature extraction, we introduced a novel edge detection algorithm based on EMD (The Earth Mover’s Distance), which works better than traditional ones. A general curvature concept was used in the measuring of image textures, which is invariant to rotation. And at last with the mathematics tools of Hough Transformation and fuzzy density function we made the last decision to determine “it is building or not”. Experiments were carried out in the Quick-bird images of Beijing, China. We were able to achieve a right detection rate larger than 75% for those buildings that are not occluded severely. * Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.