CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data
This work addresses artifact removal for scientific visualization in domains like planetary science and geology, but it is incremental as it adapts existing methods to a specific data type.
The paper tackles cloud artifact removal in satellite Digital Elevation Model (DEM) data by developing a method that combines traditional image processing with a U-Net-based deep learning approach, achieving successful performance on single-channel DEMs without requiring multi-channel spectral imagery.
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.