Deep internal learning for inpainting of cloud-affected regions in satellite imagery
Czerkawski, Mikolaj and Upadhyay, Priti and Davison, Christopher and Werkmeister, Astrid and Cardona, Javier and Atkinson, Robert and Michie, Craig and Andonovic, Ivan and Macdonald, Malcolm and Tachtatzis, Christos (2022) Deep internal learning for inpainting of cloud-affected regions in satellite imagery. Remote Sensing, 14 (6). 1342. ISSN 2072-4292 (https://doi.org/10.3390/rs14061342)
Preview |
Text.
Filename: Czerkawaski_etal_RS_2022_Deep_internal_learning_for_inpainting_of_cloud_affected_regions.pdf
Final Published Version License: Download (61MB)| Preview |
Abstract
Cloud cover remains a significant limitation to a broad range of applications relying on optical remote sensing imagery, including crop identification/yield prediction, climate monitoring, and land cover classification. A common approach to cloud removal treats the problem as an inpainting task and imputes optical data in the cloud-affected regions employing either mosaicing historical data or making use of sensing modalities not impacted by cloud obstructions, such as SAR. Recently, deep learning approaches have been explored in these applications; however, the majority of reported solutions rely on external learning practices, i.e., models trained on fixed datasets. Although these models perform well within the context of a particular dataset, a significant risk of spatial and temporal overfitting exists when applied in different locations or at different times. Here, cloud removal was implemented within an internal learning regime through an inpainting technique based on the deep image prior. The approach was evaluated on both a synthetic dataset with an exact ground truth, as well as real samples. The ability to inpaint the cloud-affected regions for varying weather conditions across a whole year with no prior training was demonstrated, and the performance of the approach was characterised.
ORCID iDs
Czerkawski, Mikolaj ORCID: https://orcid.org/0000-0002-0927-0416, Upadhyay, Priti ORCID: https://orcid.org/0000-0002-6212-5314, Davison, Christopher ORCID: https://orcid.org/0000-0002-9450-1791, Werkmeister, Astrid ORCID: https://orcid.org/0000-0002-0174-5851, Cardona, Javier ORCID: https://orcid.org/0000-0002-9284-1899, Atkinson, Robert ORCID: https://orcid.org/0000-0002-6206-2229, Michie, Craig ORCID: https://orcid.org/0000-0001-5132-4572, Andonovic, Ivan ORCID: https://orcid.org/0000-0001-9093-5245, Macdonald, Malcolm ORCID: https://orcid.org/0000-0003-4499-4281 and Tachtatzis, Christos ORCID: https://orcid.org/0000-0001-9150-6805;-
-
Item type: Article ID code: 79868 Dates: DateEvent10 March 2022Published10 March 2022Published Online8 March 2022AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Faculty of Engineering > Chemical and Process Engineering
Strategic Research Themes > Measurement Science and Enabling Technologies
Technology and Innovation Centre > Advanced Engineering and ManufacturingDepositing user: Pure Administrator Date deposited: 11 Mar 2022 02:16 Last modified: 03 Oct 2024 00:37 URI: https://strathprints.strath.ac.uk/id/eprint/79868