Volume II-3/W3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W3, 67-72, 2013
https://doi.org/10.5194/isprsannals-II-3-W3-67-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W3, 67-72, 2013
https://doi.org/10.5194/isprsannals-II-3-W3-67-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  08 Oct 2013

08 Oct 2013

Integration of spectral information and photogrammetric DSM for urban areas classification

F. Nex1, M. Dalla Mura2, and F. Remondino1 F. Nex et al.
  • 13D Optical Metrology unit, Bruno Kessler Foundation (FBK), Italy
  • 2GIPSA-Lab, Grenoble Institute of Technology, France

Keywords: Classification, Urban, DSM, Matching, Spectral

Abstract. The automated classification of urban areas in one of the main topic in the Geomatics domain. Several papers dealing with this topic have been already presented in the last decade. Most of these approaches uses multi-spectral or LiDAR data or both of them as input. In this paper, an algorithm for urban areas classification based only on overlapping RGB images is presented. The integration of radiometric and geometric information derived from aerial images is exploited in order to extract the three main classes of urban areas (i.e. building, vegetation and road) in automated way and without prior information. A photogrammetric Digital Surface Model (DSM) is firstly generated applying dense image matching techniques and this information as well as some spatial features provided by morphological filters are combined to derive a first classification. Subsequently, a thematic classification of the surveyed areas is performed considering the surface’s reflectance in the visible spectrum of the used images and the multi-image information provided by the overlapping images. Range and image information are so merged in an algorithm that allows the reciprocal and iterative sharing of information in order to increase the reliability and completeness of the classification process. After a detailed description of the algorithm, the achieved results over dense urban areas are shown and discussed.