Comparison of convolutional neural networks for cloudy optical images reconstruction from single or multitemporal joint SAR and optical images

Autor: Cresson, Rémi, Narçon, Nicolas, Gaetano, Raffaele, Dupuis, Aurore, Tanguy, Yannick, May, Stéphane, Commandre, Benjamin

With the increasing availability of optical and synthetic aperture radar (SAR) images thanks to the Sentinel constellation, and the explosion of deep learning, new methods have emerged in recent years to tackle the reconstruction of optical images th

Externí odkaz: http://arxiv.org/abs/2204.00424

MRFusion: A Deep Learning architecture to fuse PAN and MS imagery for land cover mapping

Autor: Gaetano, Raffaele, Ienco, Dino, Ose, Kenji, Cresson, Remi

Nowadays, Earth Observation systems provide a multitude of heterogeneous remote sensing data. How to manage such richness leveraging its complementarity is a crucial chal- lenge in modern remote sensing analysis. Data Fusion techniques deal with this

Externí odkaz: http://arxiv.org/abs/1806.11452

A generic framework for the development of geospatial processing pipelines on clusters

Autor: Cresson, Remi

Publikováno v: IEEE journal of geoscience and remote sensing letters, 2016

The amount of remote sensing data available to applications is constantly growing due to the rise of very-high-resolution sensors and short repeat cycle satellites. Consequently, tackling computational complexity in Earth Observation information extr

Externí odkaz: http://arxiv.org/abs/1609.08893

Sentinel-1/Sentinel-2-Derived soil moisture product at plot scale (S²MP)

Autor: Lozac'h, Loïc, Bazzi, Hassan, Baghdadi, Nicolas, Hajj, Mohammad, Zribi, Mehrez, Cresson, Rémi

Publikováno v: IEEE Geoscience and Remote Sensing Society (M2GARSS 2020)
IEEE Geoscience and Remote Sensing Society (M2GARSS 2020), Mar 2020, Tunis, Tunisia. ⟨10.1109/M2GARSS47143.2020.9105210⟩

International audience; The objective of this paper is to present an operational approach for mapping soil moisture at high spatial resolution over agricultural areas with vegetation cover. The developed approach uses the neural network (NN) techniqu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3d87d520ec997bfaf0c646ec09b8f08d
https://hal.inrae.fr/hal-02631856/document

A Two-Branch CNN Architecture for Land Cover Classification of PAN and MS Imagery

Autor: Gaetano, Raffaele, Ienco, Dino, Ose, Kenji, Cresson, Rémi

Publikováno v: Remote Sensing, Vol 10, Iss 11, p 1746 (2018)
Remote Sensing
Remote Sensing, MDPI, 2018, 10 (11), pp.1746. ⟨10.3390/rs10111746⟩

The use of Very High Spatial Resolution (VHSR) imagery in remote sensing applications is nowadays a current practice whenever fine-scale monitoring of the earth’s surface is concerned. VHSR Land Cover classification, in particular, is currently a w

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::88846f2cd857c98f79456faf1dc64dfd
https://www.mdpi.com/2072-4292/10/11/1746