M. Berger, J. Moreno, J. A. Johannessen, P. F. Levelt, and R. F. Hanssen, ESA's sentinel missions in support of Earth system science, Remote Sens. Environ, vol.120, pp.84-90, 2012.

N. Kolecka, C. Ginzler, R. Pazur, B. Price, and P. H. Verburg, Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series, Remote Sens, vol.10, 1221.

N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data, IEEE Geosci. Remote Sens. Lett, vol.14, pp.778-782, 2017.

J. Inglada, A. Vincent, M. Arias, B. Tardy, and D. Morin, Rodes, I. Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

F. Guttler, D. Ienco, J. Nin, M. Teisseire, and P. Poncelet, A graph-based approach to detect spatiotemporal dynamics in satellite image time series, ISPRS J. Photogramm. Remote Sens, vol.130, pp.92-107, 2017.
URL : https://hal.archives-ouvertes.fr/lirmm-01541930

L. Khiali, D. Ienco, and M. Teisseire, Object-oriented satellite image time series analysis using a graph-based representation, Ecol. Inform, vol.43, pp.52-64, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01741613

M. J. Steinhausen, P. D. Wagner, B. Narasimhan, and B. Waske, Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions, Int. J. Appl. Earth Obs. Geoinf, vol.73, pp.595-604, 2018.

D. H. Minh, D. Ienco, R. Gaetano, N. Lalande, E. Ndikumana et al., Deep Recurrent Neural Networks for Winter Vegetation Quality Mapping via Multitemporal SAR Sentinel-1, IEEE Geosci. Remote Sens. Lett, vol.15, pp.464-468, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01931485

Y. J. Gbodjo, D. Ienco, and L. Leroux, Toward Spatio-Spectral Analysis of Sentinel-2 Time Series Data for Land Cover Mapping, IEEE Geosci. Remote Sens. Lett, vol.17, pp.307-311, 2019.

R. Interdonato, D. Ienco, R. Gaetano, K. Ose, and . Duplo, A DUal view Point deep Learning architecture for time series classificatiOn, ISPRS J. Photogramm. Remote Sens, vol.149, pp.91-104, 2019.
URL : https://hal.archives-ouvertes.fr/lirmm-02011262

S. M. Mousavi, S. Roostaei, and H. Rostamzadeh, Estimation of flood land use/land cover mapping by regional modelling of flood hazard at sub-basin level case study: Marand basin, Geomat. Nat. Hazards Risk, vol.10, pp.1155-1175, 2019.

S. Fritz, L. See, J. C. Bayas, F. Waldner, D. Jacques et al., A comparison of global agricultural monitoring systems and current gaps, Agric. Syst, vol.168, pp.258-272, 2019.

F. Gao, J. G. Masek, M. R. Schwaller, and F. G. Hall, On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance, IEEE Trans. Geosci. Remote Sens, vol.44, pp.2207-2218, 2006.

D. Ienco, R. Interdonato, R. Gaetano, and D. H. Minh, Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture, ISPRS J. Photogramm. Remote Sens, vol.158, pp.11-22, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02609707

D. Ienco, R. Gaetano, R. Interdonato, K. Ose, and D. H. Minh, Combining Sentinel-1 and Sentinel-2 Time Series via RNN for Object-Based Land Cover Classification, Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), 2019.

G. C. Iannelli and P. Gamba, Jointly Exploiting Sentinel-1 and Sentinel-2 for Urban Mapping, Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2018), pp.8209-8212, 2018.

J. Erinjery, M. Singh, and R. Kent, Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery, Remote Sens. Environ, vol.216, pp.345-354, 2018.

K. V. Tricht, A. Gobin, S. Gilliams, and I. Piccard, Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium, Remote Sens, vol.10, p.1642, 2018.

J. Denize, L. Hubert-moy, J. Betbeder, S. Corgne, J. Baudry et al., Evaluation of using sentinel-1 and-2 time-series to identify winter land use in agricultural landscapes

R. Fernández-beltran, J. M. Haut, M. E. Paoletti, J. Plaza, A. Plaza et al., Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion, IEEE Geosci. Remote Sens. Lett, vol.15, pp.1347-1351, 2018.

D. Gregorio and A. , Land Cover Classification System: Classification Concepts and User Manual: LCCS; Food & Agriculture Organization, vol.2, 2005.

D. Sulla-menashe, M. A. Friedl, O. N. Krankina, A. Baccini, C. E. Woodcock et al., Hierarchical mapping of Northern Eurasian land cover using MODIS data, Remote Sens. Environ, vol.115, pp.392-403, 2011.

M. F. Wu, Z. C. Sun, B. Yang, and S. S. Yu, A Hierarchical Object-oriented Urban Land Cover Classification Using WorldView-2 Imagery and Airborne LiDAR data, IOP Conf. Ser. Earth Environ. Sci, vol.46, 2016.

D. Sulla-menashe, J. M. Gray, S. P. Abercrombie, and M. A. Friedl, Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product, Remote Sens. Environ, vol.222, pp.183-194, 2019.

T. Blaschke, Object based image analysis for remote sensing, ISPRS J. Photogramm. Remote Sens, vol.65, pp.2-16, 2010.

T. Lillesand, R. W. Kiefer, and J. Chipman, Remote Sensing and Image Interpretation

X. Zhu, D. Tuia, L. Mou, G. X. Zhang, F. Xu et al., Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources, IEEE Geosci. Remote Sens. Mag, vol.5, pp.8-36, 2017.

D. Ienco, R. Gaetano, C. Dupaquier, and P. Maurel, Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks, IEEE Geosci. Remote Sens. Lett, vol.14, pp.1685-1689, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01931486

L. Mou, P. Ghamisi, and X. X. Zhu, Unsupervised Spectral-Spatial Feature Learning via Deep Residual Conv-Deconv Network for Hyperspectral Image Classification, IEEE Trans. Geosci. Remote Sens, vol.56, pp.391-406, 2018.

L. Zhong, L. Hu, and H. Zhou, Deep learning based multi-temporal crop classification. Remote Sens. Environ, vol.221, pp.430-443, 2019.

M. Rußwurm and M. Körner, Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders, ISPRS Int. J. Geo-Inf, vol.7, p.129, 2018.

J. W. Rouse, R. H. Hass, J. Schell, and D. Deering, Monitoring vegetation systems in the great plains with ERTS, Proceedings of the Third Earth Resources Technology Satellite (ERTS) symposium, vol.1, pp.309-317, 1973.

S. Dupuy, R. Gaetano, and L. L. Mézo, Mapping land cover on Reunion Island in 2017 using satellite imagery and geospatial ground data

P. Lassalle, J. Inglada, J. Michel, M. Grizonnet, and J. Malik, A Scalable Tile-Based Framework for Region-Merging Segmentation, IEEE Trans. Geosci. Remote Sens, vol.53, pp.5473-5485, 2015.

K. Cho, B. Van-merrienboer, Ç. Gülçehre, D. Bahdanau, F. Bougares et al., Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01433235

D. Bahdanau, K. Cho, and Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate. arXiv, 2014.

M. Luong, H. Pham, and C. D. Manning, Effective Approaches to Attention-based Neural Machine Translation, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.1412-1421, 2015.

D. Britz, M. Y. Guan, and M. Luong, Efficient Attention using a Fixed-Size Memory Representation, 2017.

G. Karamanolakis, D. Hsu, and L. Gravano, Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health, 2019.

S. Hou, X. Liu, and Z. Wang, DualNet: Learn Complementary Features for Image Recognition, Proceedings of the IEEE International Conference on Computer Vision, pp.502-510, 2017.

P. Benedetti, D. Ienco, R. Gaetano, K. Ose, R. G. Pensa et al., Fusion: A Deep Learning Architecture for Multiscale Multimodal Multitemporal Satellite Data Fusion, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, vol.11, pp.4939-4949, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01931466

S. Valero, L. Arnaud, M. Planells, E. Ceschia, and G. Dedieu, Sentinel's Classifier Fusion System for Seasonal Crop Mapping, Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), pp.6243-6246, 2019.

C. Pelletier, G. Webb, and F. Petitjean, Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

D. P. Kingma, J. Ba, and . Adam, A Method for Stochastic Optimization. arXiv, 2014.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, pp.436-444, 2015.

L. Ma, Y. Liu, X. Zhang, Y. Ye, G. Yin et al., Deep learning in remote sensing applications: A meta-analysis and review, ISPRS J. Photogramm. Remote Sens, vol.152, pp.166-177, 2019.

A. E. Maxwell, T. A. Warner, and F. Fang, Implementation of machine-learning classification in remote sensing: An applied review, Int. J. Remote Sens, vol.39, pp.2784-2817, 2018.

H. Choi, K. Cho, and Y. Bengio, Fine-grained attention mechanism for neural machine translation, Neurocomputing, vol.284, pp.171-176, 2018.

M. T. Ribeiro, S. Singh, and C. Guestrin, Why Should I Trust You?": Explaining the Predictions of Any Classifier, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1135-1144, 2016.

G. Chen, Q. Weng, G. J. Hay, and Y. He, Geographic object-based image analysis (GEOBIA): Emerging trends and future opportunities, GISci. Remote Sens, vol.55, pp.159-182, 2018.

P. Boccardo and F. G. Tonolo, Remote sensing role in emergency mapping for disaster response, In Engineering Geology for Society and Territory, vol.5, pp.17-24, 2015.

M. Belgiu and L. Dr?gu?, Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm. Remote Sens, vol.114, pp.24-31, 2016.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones et al., Polosukhin, I. Attention is All you Need, Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, pp.4-9, 2017.

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