From Imagery to Map: Digital Photogrammetric Technologies 12 th International Scientific and Technical Conference From Imagery to Map: Digital Photogrammetric Technologies September 24–27, 2012, Algarve, Portugal Modern DSM creation algorithms in DPW’s Andrey Sechin Scientific Director, Racurs

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

Local methods

Local methods: advantages & problems Pros: no special memory requirements fast subpixel accuracy in “smooth” regions. Cons: problems in untextured regions, with horizontal patterns, with repetitive patterns discontinuities – the main problem.

Local methods: example GeoEye-1 (0.5m) stereo satellite imagery

Global methods E = E(data) + E(smooth) E(data)- measures photo consistency E(smooth)- measures smoothness. Penalties for small and large disparity changes NP hard problem Two epipolar images are considered

Global methods: dynamic programming We cut energy function into scan lines. This gives very fast computing speed. The result is horizontal streaks in the disparity map.

Global methods: graph-cuts & belief propagation We minimize energy approximately. Graph-cuts are not computationally efficient. Belief propagation generally does not converge but can computationally be very efficient.

Global methods: SGM & simple tree We approximate energy and find the best solution strictly. Memory requirements for SGM = w*h*dmax. Filtering and smoothing is required. Special algorithms and modifications for multiple images, or for pushbroom sensors.

Global methods: PHOTOMOD approach 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

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