Abstract
Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define. In this paper, we describe a method for creating cloud artifact masks which can be used to remove artifacts from satellite imagery using a combination of traditional image processing together with deep learning based on U-Net. Compared to previous methods, our approach does not require multi-channel spectral imagery but performs successfully on single-channel Digital Elevation Models (DEMs). DEMs are a representation of the topography of the Earth and have a variety applications including planetary science, geology, flood modeling, and city planning.
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Borkiewicz, K., Shah, V., Naiman, J. P., Shen, C., Levy, S., & Carpenter, J. (2021). CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data. In Proceedings - 2021 IEEE Visualization Conference - Short Papers, VIS 2021 (pp. 1–5). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/VIS49827.2021.9623327
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