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Internal Learning for Sequence-to-Sequence Cloud Removal via Synthetic Aperture Radar Prior Information

Ebel, Patrick and Schmitt, Michael and Zhu, Xiao Xiang (2021) Internal Learning for Sequence-to-Sequence Cloud Removal via Synthetic Aperture Radar Prior Information. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2691-2694. IGARSS 2021, 11.-16.7.21, Brussels, virtuell. doi: 10.1109/IGARSS47720.2021.9554268.

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Official URL: https://ieeexplore.ieee.org/document/9554268


Many observations acquired via optical satellites are polluted by cloud coverage, impeding a continuous and on-demand monitoring of the Earth. Recent advances in the field of cloud removal consider multi-temporal data to reconstruct pixels covered by clouds at a time point of interest. Yet, the limitation of preceding work is that information gets integrated over time, removing any temporal resolution from the de-clouded end products. In this work we consider a sequence-to-sequence approach, translating cloudy time series to a series of cloud-free multi-spectral images without the need of any external cloud-free data set. Our network is guided by synthetic aperture radar (SAR) information providing a strong prior for the reconstruction of cloud-covered information. We analyze the proposed method by visual inspection of predictions and in terms of error metrics to highlight its benefits. Finally, an ablation study is conducted in which the our network is compared against a baseline model and the effectiveness of the proposed SAR prior is demonstrated.

Item URL in elib:https://elib.dlr.de/146242/
Document Type:Conference or Workshop Item (Lecture)
Title:Internal Learning for Sequence-to-Sequence Cloud Removal via Synthetic Aperture Radar Prior Information
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Ebel, Patrickpatrick.ebel (at) tum.deUNSPECIFIED
Schmitt, MichaelMichael.Schmitt (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Date:July 2021
Journal or Publication Title:International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1109/IGARSS47720.2021.9554268
Page Range:pp. 2691-2694
Keywords:Cloud removal, AI4EO, Remote Sensing, SAR,
Event Title:IGARSS 2021
Event Location:Brussels, virtuell
Event Type:international Conference
Event Dates:11.-16.7.21
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Rösel, Anja
Deposited On:29 Nov 2021 08:04
Last Modified:01 Dec 2021 11:08

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