CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866909372785885184 |
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| author | Borkiewicz, Kalina Shah, Viraj Naiman, J. P. Shen, Chuanyue Levy, Stuart Carpenter, Jeff |
| author_facet | Borkiewicz, Kalina Shah, Viraj Naiman, J. P. Shen, Chuanyue Levy, Stuart Carpenter, Jeff |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2110_13819 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data Borkiewicz, Kalina Shah, Viraj Naiman, J. P. Shen, Chuanyue Levy, Stuart Carpenter, Jeff Computer Vision and Pattern Recognition Graphics Machine Learning Image and Video Processing 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. |
| title | CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data |
| topic | Computer Vision and Pattern Recognition Graphics Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2110.13819 |