1 From Imagery to Map: Digital Photogrammetric Technologies 14 th International Scientific and Technical Conference From Imagery to Map: Digital Photogrammetric Technologies Dense DSM Generation Module in PHOTOMOD 6.0 Andrey Sechin Scientific Director, Racurs October 2014, Hainan, China

2 DEM, DTM, DSM, nDSM DEM, DTM have different definitions in different countries. In Russian (по-русски) ЦМР, ЦМП DEM & DTM - bare earth terrain. DSM include tree canopy & buildings. nDSM = DSM - DTM

3 PHOTOMOD. Different models (depending on algorithm) Automatic DSM (old cross-correlation algorithm) with filtering buildings and trees Automatic DSM (old cross-correlation algorithm) 3D semi-automatic model (with manual stereo vectorization) Different models for Novokuznetsk city GeoEye-1 stereopair (GSD 0.5m) GeoEye-1 stereopair (GSD 0.5m)

4 Local algorithms of DTM creation Memory efficient Fast Subpixel accuracy in “smooth” regions Problems with periodic structures and poorly textured regions Big problem with discontinuities on images

5 Global algorithms of DSM creation Global energy minimization Take into account discontinuities and hidden surfaces Not memory efficient Still require filtering and smoothing in the end of algorithm E = E(data) + E(smooth) Semi Global Matching (SGM) Graph-cuts Simple Tree Iterative- deformation method (RACURS)

6 Local vs Global method SGM and Iterative deformation methods CrossCorrelation

7 PHOTOMOD: iterative deformation method (IDM) All images are taken into account simultaneously Memory efficient Image pyramid hierarchy is used for speed and reliability Image resection is used to calculate occlusions Still requires filtering and smoothing on the final step

8 height approximation levels 1-st image2-nd image DSM orthophoto Point with unknown height PHOTOMOD: iterative deformation method (IDM)

9 IDM vs SGM We used SURE (Institute for Photogrammetry (IfP), University of Stuttgart) as SGM example SGM is faster (20-30%) on the local computer IDM is faster in the network environment (parallel computing based on images + dsm levels and area splitting) IDM does not need epipolar geometry IDM uses all images simultaneously IDM uses different strategies based on the DSM guess IDM uses elements of pattern recognition for different height approximations

10 PHOTOMOD User interface

11 IDM: GeoEye example

12 IDM: GeoEye example

13 IDM: WorldView 1 example

14 IDM: WorldView 1 example

15 IDM: UltraCam example

16 IDM: UltraCam example

17 IDM: UltraCam example

18 u IDM: DMC example (Munich block)

19 u IDM: DMC example

20 u Thank you