Search Results for author: Raffaele Gaetano

Found 9 papers, 2 papers with code

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

1 code implementation1 Apr 2022 Rémi Cresson, Nicolas Narçon, Raffaele Gaetano, Aurore Dupuis, Yannick Tanguy, Stéphane May, Benjamin Commandre

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 that are impacted by clouds.

Image Reconstruction

Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationships

1 code implementation20 Nov 2019 Yawogan Jean Eudes Gbodjo, Dino Ienco, Louise Leroux, Roberto Interdonato, Raffaele Gaetano, Babacar Ndao, Stephane Dupuy

European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at highspatial resolution and high revisit time, respectively, radar and optical imagesthat support a wide range of Earth surface monitoring tasks such as LandUse/Land Cover mapping.

Specificity Time Series Analysis

Supervised level-wise pretraining for recurrent neural network initialization in multi-class classification

no code implementations4 Nov 2019 Dino Ienco, Roberto Interdonato, Raffaele Gaetano

To the best of our knowledge, despite the great interest in RNN-based classification, this is the first data-aware strategy dealing with the initialization of such models.

Classification General Classification +3

Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based land cover classification

no code implementations13 Dec 2018 Dino Ienco, Raffaele Gaetano, Roberto Interdonato Kenji Ose, Dinh Ho Tong Minh

Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobility, land management planning and land cover monitoring.

General Classification Management +1

DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn

no code implementations20 Sep 2018 Roberto Interdonato, Dino Ienco, Raffaele Gaetano, Kenji Ose

In this work, we propose the first deep learning architecture for the analysis of SITS data, namely \method{} (DUal view Point deep Learning architecture for time series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity.

General Classification Time Series Analysis +1

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

no code implementations29 Jun 2018 Raffaele Gaetano, Dino Ienco, Kenji Ose, Remi Cresson

Common techniques to produce land cover maps from such VHSR images typically opt for a prior pansharpening of the multi-resolution source for a full resolution processing.


Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1

no code implementations11 Aug 2017 Dinh Ho Tong Minh, Dino Ienco, Raffaele Gaetano, Nathalie Lalande, Emile Ndikumana, Faycal Osman, Pierre Maurel

The objective of this paper is to provide a better understanding of the capabilities of radar Sentinel-1 and deep learning concerning about mapping winter vegetation quality coverage.

Time Series Analysis

Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks

no code implementations13 Apr 2017 Dino Ienco, Raffaele Gaetano, Claire Dupaquier, Pierre Maurel

Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time.

Classification General Classification +2

Cannot find the paper you are looking for? You can Submit a new open access paper.