Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 503-509, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/503/2016/
doi:10.5194/isprs-archives-XLI-B3-503-2016
 
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 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.
Conference paper (PDF, 1363 KB)


Citation: de Vieilleville, F., Ristorcelli, T., and Delvit, J.-M.: DEM RECONSTRUCTION USING LIGHT FIELD AND BIDIRECTIONAL REFLECTANCE FUNCTION FROM MULTI-VIEW HIGH RESOLUTION SPATIAL IMAGES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 503-509, doi:10.5194/isprs-archives-XLI-B3-503-2016, 2016.

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