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We present the workflow of a DSM refinement methodology using a Hybrid-cGANwhere the generative part consists of two encoders and a common decoder whichblends the spectral and height information within one network. The inputs tothe Hybrid-cGAN are single-channel photogrammetric DSMs with continuous valuesand single-channel pan-chromatic (PAN) half-meter resolution satellite images.Experimental results demonstrate that the earlier information fusion from datawith different physical meanings helps to propagate fine details and completean inaccurate or missing 3D information about building forms. Moreover, itimproves the building boundaries making them more rectilinear.

Late or Earlier Information Fusion from Depth and Spectral Data? Large-Scale Digital Surface Model Refinement by Hybrid-cGAN