Volume XLI-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 503-509, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-503-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 503-509, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-503-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  09 Jun 2016

09 Jun 2016

DEM RECONSTRUCTION USING LIGHT FIELD AND BIDIRECTIONAL REFLECTANCE FUNCTION FROM MULTI-VIEW HIGH RESOLUTION SPATIAL IMAGES

F. de Vieilleville1, T. Ristorcelli2, and J.-M. Delvit3 F. de Vieilleville et al.
  • 1MAGELLIUM SAS, Dept. Earth Observation, Ramonville-Saint-Agne, France
  • 2MAGELLIUM SAS, Dept. Imaging and Applications, Ramonville-Saint-Agne, France
  • 3CNES, Dept. SIQI , Toulouse, France

Keywords: DSM reconstruction, BRDF, Light field, Image sequence

Abstract. This paper presents a method for dense DSM reconstruction from high resolution, mono sensor, passive imagery, spatial panchromatic image sequence. The interest of our approach is four-fold. Firstly, we extend the core of light field approaches using an explicit BRDF model from the Image Synthesis community which is more realistic than the Lambertian model. The chosen model is the Cook-Torrance BRDF which enables us to model rough surfaces with specular effects using specific material parameters. Secondly, we extend light field approaches for non-pinhole sensors and non-rectilinear motion by using a proper geometric transformation on the image sequence. Thirdly, we produce a 3D volume cost embodying all the tested possible heights and filter it using simple methods such as Volume Cost Filtering or variational optimal methods. We have tested our method on a Pleiades image sequence on various locations with dense urban buildings and report encouraging results with respect to classic multi-label methods such as MIC-MAC, or more recent pipelines such as S2P. Last but not least, our method also produces maps of material parameters on the estimated points, allowing us to simplify building classification or road extraction.