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Late or Earlier Information Fusion From Depth and Spectral Data? Large-Scale Digital Surface Model Refinement by Hybrid-CGAN
AbstractWe present the workflow of a digital surface model (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.
Related Material
[pdf] [dataset][bibtex]@InProceedings{Bittner_2019_CVPR_Workshops,
author = {Bittner, Ksenia and Reinartz, Peter and Korner, Marco},
title = {Late or Earlier Information Fusion From Depth and Spectral Data? Large-Scale Digital Surface Model Refinement by Hybrid-CGAN},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}
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