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Recurrent neural network for rain estimation using commercial microwave links / Hai Victor Habi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3672 - 3681 Titre : Recurrent neural network for rain estimation using commercial microwave links Type de document : Article/Communication Auteurs : Hai Victor Habi, Auteur ; Hagit Messer, Auteur Année de publication : 2021 Article en page(s) : pp 3672 - 3681 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] dégradation du signal [Termes IGN] eau pluviale [Termes IGN] méthode robuste [Termes IGN] précision de l'estimation [Termes IGN] réseau neuronal récurrent Résumé : (Auteur) The use of recurrent neural networks (RNNs) to utilize measurements from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies focused on the performance of methods for wet–dry classification, here we propose an RNN algorithm for estimating the rain-rate. We empirically analyzed the proposed algorithm, using real data, and compared it with the traditional power-law (PL)-based algorithm, commonly used for estimating rain from CML attenuation measurements. Our analysis shows that the data-driven RNN algorithm, when properly trained, outperforms the PL algorithm in terms of accuracy. On the other hand, the PL algorithm is simpler and more robust when dealing with a large variety of corruptions and adverse conditions. We then introduced a time normalization (TN) layer for controlling the trade-off between performance and robustness of the RNN methods, and demonstrated its performance. Numéro de notice : A2021-337 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3010305 Date de publication en ligne : 30/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3010305 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97568 [article]