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

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Apr 22, 2019
Ksenia Bittner, Marco Körner, Peter Reinartz
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We present the workflow of a DSM refinement methodology using a Hybrid-cGAN where the generative part consists of two encoders and a common decoder which blends the spectral and height information within one network. The inputs to the Hybrid-cGAN are single-channel photogrammetric DSMs with continuous values and single-channel pan-chromatic (PAN) half-meter resolution satellite images. Experimental results demonstrate that the earlier information fusion from data with different physical meanings helps to propagate fine details and complete an inaccurate or missing 3D information about building forms. Moreover, it improves the building boundaries making them more rectilinear.

* 8 pages, This work was accepted to be presented at the IEEE/ISPRS Workshop on Large Scale Computer Vision for Remote Sensing Imagery (EarthVision) to be held at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019  
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