Sentinel-1 and Sentinel-2 Spatio-Temporal Data Fusion for Clouds Removal

• URL: http://arxiv.org/abs/2106.12226v1
• Date: Wed, 23 Jun 2021 08:15:01 GMT
• Title: Sentinel-1 and Sentinel-2 Spatio-Temporal Data Fusion for Clouds Removal
• Authors: Alessandro Sebastianelli, Artur Nowakowski, Erika Puglisi, Maria Pia Del Rosso, Jamila Mifdal, Fiora Pirri, Pierre Philippe Mathieu and Silvia Liberata Ullo
• Abstract summary: A novel method for clouds-corrupted optical image restoration has been presented and developed based on a joint data fusion paradigm. It is worth highlighting that the Sentinel code and the dataset have been implemented from scratch and made available to interested research for further analysis and investigation.
• Score: 51.9654625216266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The abundance of clouds, located both spatially and temporally, often makes remote sensing applications with optical images difficult or even impossible. In this manuscript, a novel method for clouds-corrupted optical image restoration has been presented and developed, based on a joint data fusion paradigm, where three deep neural networks have been combined in order to fuse spatio-temporal features extracted from Sentinel-1 and Sentinel-2 time-series of data. It is worth highlighting that both the code and the dataset have been implemented from scratch and made available to interested research for further analysis and investigation.

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