Using Deep Learning for Restoration of Precipitation Echoes in Radar Data
| Autor: | Cécile Mallet, Yvon Lemaître, Laurent Barthès, Nicolas Viltard, Lucie Rottner, Camille Ly, Pierre Lepetit |
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| Přispěvatelé: | Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), SPACE - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Quantitative precipitation estimation
restoration Computer science 0211 other engineering and technologies Blind inpainting 02 engineering and technology [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology law.invention law Radar imaging fully convolutional networks Electrical and Electronic Engineering Radar image segmentation 021101 geological & geomatics engineering polarimetry weak supervision [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph] Ground truth business.industry Deep learning Supervised learning deep learning Pattern recognition Image segmentation meteorological radar General Earth and Planetary Sciences Clutter Artificial intelligence business |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2022, 60, pp.5100914. ⟨10.1109/TGRS.2021.3052582⟩ |
| ISSN: | 0196-2892 |
| DOI: | 10.1109/TGRS.2021.3052582⟩ |
| Popis: | International audience; Raw data issued from meteorological radars are often corrupted by unwanted signals generically called clutter. Hills, tall buildings, atmospheric turbulence, birds, and insects yield patterns that complicate the interpretation of radar images and might add bias in the quantitative precipitation estimates (QPE). Clutter differs from precipitating echoes by both their polarimetric signatures and their particular shapes. This work deals with the removal of clutter. The core idea is to use a fully convolutional network (FCN) to take clutter shapes into account. For a straightforward approach by supervised learning, one would need radar images and their cleaned counterpart, which are not available. We developed a weakly supervised learning method that allows circumventing this issue. This method only requires auxiliary data from rain gauges. The additivity of the reflectivity allows presenting the learning problem in the form of a supervised restoration task with noisy targets. This problem is solved by successive training of a standard FCN (U-net). As ground truth is missing, standard metrics cannot be employed to make a global evaluation. Nevertheless, our method is quantitatively assessed on two clutter classes: ground clutter and interferences. A case study completes the evaluation. A qualitative comparison with the Météo-France algorithm is also performed on a couple of difficult cases. |
| Databáze: | OpenAIRE |
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