Robust Detection of Buildings in Digital Surface Models

The classification of land use, degree of development, and the location of major features, such as buildings and expanses of vegetation are of considerable interest to planning bodies, relief agencies, and the military. Landscape characteristics are under continuous modification, especially in urban and suburban areas, and therefore the maps of these areas need regular updating. Derived databases, such as land use maps and 3D city models, are used in several applications including change detection and urban planning [7,5]. Tn this paper, we use Digital Surface Models (DSM) with lm, ground resolution to detect and discriminate buildings and vegetation. The DSM is a 2.5D matrix representation of relief generated from data obtained using scanning laser altimeters (LTDAR). Huising et al. [6] demonstrated the potential of laser data for the extraction of topographic information. Several techniques have been proposed for detecting buildings in aerial imagery. However, strategies relying entirely on image data are often sensitive to brightness variations or require additional information. Being dense and independent of ambient lighting, DSM data are better suited to this purpose [lo, 21. Haala [5] located buildings by searching for local maxima, Eckstein [3] suggested a dual rank filter for extracting spatial objects and Jaynes et al. [7] used both optical and range data. Weidner et al. [lo] used morphological opening; Brunn and Weidner [ll added Bayesian nets. Kilian et al. [Sl noted that the size of the structuring element used in opening [lo, 11 to obtain an estimate of the topographic surface is critical. Furthermore, the appmximation converges to the underlying topographic surface near the objects only when it is relatively smoother than the surface with the objects included. Buildings sited on coarse terrain are not identified during initial segmentation. Vosselman [9] proposed a technique to filter out spatial objects from laser data for generating Digital Elevation Models (DEM). Essentially, the complement of this approach can be adapted to our purpose. Since this method avoids the approximation step, there are fewer errors.