CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data

• URL: http://arxiv.org/abs/2110.13819v1
• Date: Tue, 26 Oct 2021 16:15:17 GMT
• Title: CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data
• Authors: Kalina Borkiewicz, Viraj Shah, J.P. Naiman, Chuanyue Shen, Stuart Levy, Jeff Carpenter
• Abstract summary: We describe a method for creating cloud artifact masks which can be used to remove artifacts from satellite imagery. 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.
• Score: 2.586482458060451
• License: http://creativecommons.org/licenses/by/4.0/
• 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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