A. V. Ryzhkov and D. S. Zrnic, Polarimetric rainfall estimation in the presence of anomalous propagation, Journal of Atmospheric and Oceanic Technology, vol.15, issue.6, pp.1320-1330, 1998.

R. Cornelius, R. Gagon, and F. Pratte, Optimization of wsr-88d clutter processing and ap clutter mitigation, Final Report, 1995.

V. Lakshmanan, K. Hondl, G. Stumpf, and T. Smith, Quality control of weather radar data using texture features and a neural network, Preprints, 31st Radar Conference, pp.522-525, 2003.

M. Steiner and J. A. Smith, Use of three-dimensional reflectivity structure for automated detection and removal of nonprecipitating echoes in radar data, Journal of Atmospheric and Oceanic Technology, vol.19, issue.5, pp.673-686, 2002.

J. J. Gourley, P. Tabary, J. Parent, and . Chatelet, A fuzzy logic algorithm for the separation of precipitating from nonprecipitating echoes using polarimetric radar observations, Journal of Atmospheric and Oceanic Technology, vol.24, issue.8, pp.1439-1451, 2007.

Y. Cho, G. W. Lee, K. Kim, and I. Zawadzki, Identification and removal of ground echoes and anomalous propagation using the characteristics of radar echoes, Journal of Atmospheric and Oceanic Technology, vol.23, issue.9, pp.1206-1222, 2006.

V. Chandrasekar, R. Keränen, S. Lim, and D. Moisseev, Recent advances in classification of observations from dual polarization weather radars, Atmospheric Research, vol.119, pp.97-111, 2013.

J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.3431-3440, 2015.

X. Mao, C. Shen, and Y. Yang, Image restoration using convolutional auto-encoders with symmetric skip connections, 2016.

T. Halperin, A. Ephrat, and Y. Hoshen, Neural separation of observed and unobserved distributions, 2018.

H. Wang, Y. Ran, Y. Deng, and X. Wang, Study on deep-learning-based identification of hydrometeors observed by dual polarization doppler weather radars, EURASIP Journal on Wireless Communications and Networking, vol.2017, issue.1, p.173, 2017.

Y. Tao, X. Gao, K. Hsu, S. Sorooshian, and A. Ihler, A deep neural network modeling framework to reduce bias in satellite precipitation products, Journal of Hydrometeorology, vol.17, issue.3, pp.931-945, 2016.

Y. Tao, X. Gao, A. Ihler, S. Sorooshian, and K. Hsu, Precipitation identification with bispectral satellite information using deep learning approaches, Journal of Hydrometeorology, vol.18, issue.5, pp.1271-1283, 2017.

R. Bechini and V. Chandrasekar, A semisupervised robust hydrometeor classification method for dual-polarization radar applications, Journal of Atmospheric and Oceanic Technology, vol.32, issue.1, pp.22-47, 2015.

N. Besic, J. Grazioli, M. Gabella, U. Germann, and A. Berne, Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmospheric Measurement Techniques, vol.9, issue.9, 2016.

P. Zhang, W. Liu, H. Wang, Y. Lei, and H. Lu, Deep gated attention networks for large-scale street-level scene segmentation, Pattern Recognition, vol.88, pp.702-714, 2019.

Y. P. Liu, J. Shan-pan, and Z. Su, Deep blind image inpainting, CoRR, 2017.

J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras et al., Noise2noise: Learning image restoration without clean data, 2018.

A. Hertz, S. Fogel, R. Hanocka, R. Giryes, and D. Cohen-or, Blind visual motif removal from a single image, 2019.

P. Xiao, Y. Guo, and P. Zhuang, Removing stripe noise from infrared cloud images via deep convolutional networks, IEEE Photonics Journal, vol.10, issue.4, pp.1-14, 2018.

A. Pajot, E. De-bezenac, and P. Gallinari, Unsupervised adversarial image reconstruction, 2018.

B. Frénay and M. Verleysen, Classification in the presence of label noise: a survey, IEEE transactions on neural networks and learning systems, vol.25, pp.845-869, 2013.

S. Sukhbaatar, J. Bruna, M. Paluri, L. Bourdev, and R. Fergus, Training convolutional networks with noisy labels, 2014.

D. Rolnick, A. Veit, S. Belongie, and N. Shavit, Deep learning is robust to massive label noise, 2017.

N. Heller, J. Dean, and N. Papanikolopoulos, Imperfect segmentation labels: How much do they matter?" in Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp.112-120, 2018.

O. Petit, N. Thome, A. Charnoz, A. Hostettler, and L. Soler, Handling missing annotations for semantic segmentation with deep convnets, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp.20-28, 2018.

C. E. Brodley and M. A. Friedl, Identifying mislabeled training data, Journal of artificial intelligence research, vol.11, pp.131-167, 1999.

S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan et al., Training deep neural networks on noisy labels with bootstrapping, 2014.

Y. Wang, W. Liu, X. Ma, J. Bailey, H. Zha et al., Iterative learning with open-set noisy labels, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.8688-8696, 2018.

J. Li, Y. Wong, Q. Zhao, and M. Kankanhalli, Learning to learn from noisy labeled data, vol.12, 2018.

M. D. Dilmi, Méthode de classification des séries temporelles: applicationà un réseau de pluviomètres, 2019.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, pp.234-241, 2015.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp.249-256, 2010.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

J. Donahue, P. Krähenbühl, and T. Darrell, Adversarial feature learning, 2016.