291 citations · 331 across the 10 of their papers we have counts for
12 papers
Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover Mapping
Babak Ghassemi, Cassio Fraga-Dantas, Raffaele Gaetano +4
Land use and land cover mapping from Earth Observation (EO) data is a critical tool for sustainable land and resource management. While advanced machine learning and deep learning…
Semi Supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-Labelling
Cassio F. Dantas, Raffaele Gaetano, Dino Ienco
Semi-supervised domain adaptation methods leverage information from a source labelled domain with the goal of generalizing over a scarcely labelled target domain. While this settin…
Comparison of convolutional neural networks for cloudy optical images reconstruction from single or multitemporal joint SAR and optical images
Rémi Cresson, Nicolas Narçon, Raffaele Gaetano +4
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 eme…
Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation
Dino Ienco, Yawogan Jean Eudes Gbodjo, Roberto Interdonato +1
Nowadays, modern Earth Observation systems continuously collect massive amounts of satellite information. The unprecedented possibility to acquire high resolution Satellite Image T…
Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationships
Yawogan Jean Eudes Gbodjo, Dino Ienco, Louise Leroux +4
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 r…
Supervised level-wise pretraining for recurrent neural network initialization in multi-class classification
Dino Ienco, Roberto Interdonato, Raffaele Gaetano
Recurrent Neural Networks (RNNs) can be seriously impacted by the initial parameters assignment, which may result in poor generalization performances on new unseen data. With the o…
Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based land cover classification
Dino Ienco, Raffaele Gaetano, Roberto Interdonato +2
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, mobi…
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
Roberto Interdonato, Dino Ienco, Raffaele Gaetano +1
Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spa…
MRFusion: A Deep Learning architecture to fuse PAN and MS imagery for land cover mapping
Raffaele Gaetano, Dino Ienco, Kenji Ose +1
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…
M3Fusion: A Deep Learning Architecture for Multi-{Scale/Modal/Temporal} satellite data fusion
P. Benedetti, D. Ienco, R. Gaetano +3
Modern Earth Observation systems provide sensing data at different temporal and spatial resolutions. Among optical sensors, today the Sentinel-2 program supplies high-resolution te…
Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1
Dinh Ho Tong Minh, Dino Ienco, Raffaele Gaetano +4
Mapping winter vegetation quality coverage is a challenge problem of remote sensing. This is due to the cloud coverage in winter period, leading to use radar rather than optical im…
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks
Dino Ienco, Raffaele Gaetano, Claire Dupaquier +1
Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to effici…