AUTOMATIC EXTRACTION AND MODELLING OF URBAN BUILDINGS FROM HIGH RESOLUTION AERIAL IMAGES Matthieu Cord*, Michel Jordan*, Jean-Pierre Cocquerez*, Nicolas Paparoditis (*) ETIS, URA CNRS 8051 6, avenue du Ponceau ´ F 95014 Cergy-Pontoise Cedex, France E-mail: jordan,cord,cocquerez @ensea.fr
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( ) IGN – LOEMI 2, av. Pasteur ´ F 94165 Saint-Mande´ Cedex, France E-mail:
[email protected]
Commission II, Working Group 6
KEY WORDS: Aerial images, Stereo processing, Digital elevation model, Urban model, Building detection
ABSTRACT In this paper, a strategy is presented to automatically generate 3D building models in urban sites from high resolution (8cm per pixel) aerial stereopairs. The entire system is based on a close cooperation between 2D and 3D processings. We have adopted a hierarchical approach — global scene analysis followed by local modelling — in order to deal with complex urban scenes. Our global scene analysis exploits to the full extent the depth information: an adaptative correlation algorithm provides dense and reliable depth data, then we elaborate a global scene classification in three classes: “ground”, “building” and “vegetation”. The local modelling is then carried out working separately on each “building” regions. It consists in a roof boundary detection and modelling, from linear segments and 3D contours. Roof boundaries are finally vectorized, providing accurate building models. ´ RESUM E´ ` ˆ ` Nous pr´esentons dans cet article une strat´egie pour l’´elaboration automatique de modeles de batiments dans des scenes ´ urbaines, a` partir de couples st´er´eoscopiques d’images aeriennes noir et blanc a` haute r´esolution (8 cm par pixel). ´ ´ L’ensemble du processus repose sur la cooperation etroite entre informations 2D et 3D. Nous avons adopt´e une approche ` puis modelisation ´ ` hi´erarchique, analyse globale de la scene locale, afin de traiter la complexit´e des scenes urbaines. ´ d’altitude L’analyse globale s’appuie sur l’information d’altitude : un algorithme de corr´elation adaptative fournit des donnees ` ˆ et “veg ´ etation”. ´ denses et fiables ; nous construisons ensuite une classification de la scene en trois classes : “sol”, “bati” ´ ´ separ´ ´ ˆ ´ La modelisation locale est ensuite effectuee ement pour chaque r´egion de type “bati”. Elle consiste en la detection et ´ ´ ´ et la modelisation des bords de toit, a` partir de segments et de contours 3D. Les bords de toit sont finalement linearis es, ` ˆ fournissent ainsi des modeles pr´ecis du bati.
1 INTRODUCTION Various application fields, such as urbanism, environmental modelling, telecommunications, etc., need accurate and up-to-date cartographic informations, either as maps or databases. In this context, man-made features (roads, buildings) have been widely studied, especially in urban areas (Gr¨un et al., 1995)(Gr¨un et al., 1997). Data come from aerial imagery. Height information can be retrieved either from stereophotogrammetry or from airborne laser acquisition. High resolution aerial images and digital colour camera images allow accurate cartographic information extraction in dense urban areas, by mean of specially dedicated algorithms (Henricsson, 1998)(Haala and Brenner, 1998)(Girard et al., 1998). Cartographic applications especially concern semi-automatic data-bases elaboration and updating. We present in this paper a set of algorithms for building detection and modelling from high resolution monochromatic aerial image pair. Figure 1 shows our system synoptic scheme: we have defined a hierarchical approach, from a global scene analysis to a fine local modelling. We believe that one of the most important information for building detection is the altitude, so we first compute a digital elevation model (DEM) from the image pair (section 2); then
buildings are detected from this DEM, as height blobs; after detection, height blobs are classified as “vegetation” or “buildings” (section 3); building boundaries are separately modelled for each building (section 4). Section 5 presents some results, and we finally conclude in section 6.
2 DIGITAL ELEVATION MODEL COMPUTATION DEM properties such as density, reliability, accuracy and depth discontinuities localisation are a key point for building detection and reconstruction. Edge — or other points of interest — matching preserves depth discontinuities but provides sparse results. On the opposite, area-based techniques usually provide dense disparity maps; unfortunately, the fixed template size matching is not able to render well located depth discontinuities. To overcome this problem, adaptive size templates have been carried out (Kanade and Okutomi, 1994)(Lotti and Giraudon, 1994). We recently developed an adaptive matching based on a contour adaptive window shaping technique (Cord et al., 1998). We develop in this section a DEM processing scheme that focuses on the improvement of our first adaptive method in the case of large window sizes and broken contour lines.
EAST Image
P2
WEST Image
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DEM Computation
P3
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Global scene classification
Ground Regions
Building Regions
Vegetation Regions
Figure 3: Geodetic adaptive correlation scheme. Each pixel of the window has a weight value depending on its distance from the central pixel P . The weight value of the pixel P is much smaller than the value of the pixel P because of the P; P geodetic distance (without crossing contours) is quite larger than the P; P distance.
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Building modelling
Building Model
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w adjusts the template size and the weighting influence. Moreover, instead of the classical template similarity measurement functions, we use a gradient template similarity function (Crouzil et al., 1996). It is less sensitive to noise and the peak of the correlation curve stands out better, especially when the template windows are large and the depth discontinuities are sharp. Our geodetic adaptive gradient template similarity function Fcor between pixel i; j of image I1 (with gradient vector image G1 ) and pixel i k; j l of image I2 (with gradient vector image G2 ) can be expressed as:
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cor
Figure 2: Contour adaptive template scheme limitation. When the contour line is broken, all the points of the window are used for the correlation score computation.
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with dgeod P1; P2 the shortest way between P1 and P2 without crossing any contours.
2.1 High resolution image matching scheme for urban landscapes We have introduced an adaptive shape window matching using contour image features to define the window shape: only the pixels on the same side of a contour and connected to the centre pixel are used for the correlation score (Paparoditis et al., 1998). The depth discontinuities are thus preserved and well located. However, this method is not efficient when the contour line is broken (figure 2); in this case, we loose the adaptive aspect of this correlation method.
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Figure 1: Synoptic scheme.
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Anyway, for high resolution image matching, large window sizes (usually more than 13 13) are necessary to take into account the poorly textured surfaces. It is thus interesting to use a template weighting function to reinforce the influence of the central pixels (Schultz, 1995). Usually, gaussian distribution functions are used to calculate the template weights.
We propose a new adaptive correlation scheme based on a cooperation between our adaptive shape technique and gaussian weighting template correlation methods. The idea is to prevent the diffusion effects due to the contour discontinuities. We change the classical isotropic gaussian weigthing for a geodetic weighting propagating on all intercontour area (figure 3). Our geodetic adaptive template Mga is built for each pixel i; j of image I1 (slave image) as following:
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Multi-resolution matching strategy is used most of the time with template-based matching techniques to overcome computational problems (Hannah, 1989)(Leloglu et al., 1998). It allows to take into account constraints on the disparity search intervals and on the disparity surface shape. Adaptive window shaping techniques have to be efficiently combined with multi-scale matching processing. Our multiresolution strategy is based on the geodetic adaptive matching technique applied at each level of the multi-resolution process. It is coupled with a validation process to avoid the matching error propagation. We use a symmetric validation based on the two way filtering technique (Fua, 1991). At the finest level, a local polynomial interpolation of the correlation surface around the highest peak is carried out to improve the disparity measurement (figure 4).
I1 Pyramid
I2 Pyramid
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two neighbouring pixels is less than a threshold, they are merged in the same region. Pixels having no altitude (the ones having no corresponding point in images) are not considered in this calculation. The altitude threshold is chosen so to control the maximal possible slope of homogeneous regions. This method gives independent from processing order results, and the obtained regions not necessary are planar or horizontal.
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Geodesic adaptative matching
I1 Disparity Resolution = k Initial I1 Disparity Resolution = k
I2 Disparity Resolution = k Initial I2 Disparity Resolution = k
Symmetrical internal validation
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