Integration of photogrammetric DSM and advanced image analysis for the classification of urban areas

In this paper, a technique for the integration of images and point cloud for urban areas classification is presented. A set of aerial RGB overlapping images are used as input. A photogrammetric Digital Surface Model (DSM) is firstly generated by using advanced matching techniques. Subsequently, a thematic classification of the surveyed areas is performed considering simultaneously the surface’s reflectance in the visible spectrum of the image sequence, the altitude information (provided by the generated DSM) and additional spatial features (Attribute Profiles). Exploiting the geometrical constraints provided by the collinearity condition and the epipolar geometry between the images, the thematic classification of the land cover can be improved by considering simultaneously the height information and the reflectance values of the DSM. Examples and comments of the proposed classification algorithm are given using a set of aerial images over a dense urban area.