291 citations · 333 across the 14 of their papers we have counts for
10 papers · 1 filter
Masking Strategies for Background Bias Removal in Computer Vision Models
Ananthu Aniraj, Cassio F. Dantas, Dino Ienco +1
Models for fine-grained image classification tasks, where the difference between some classes can be extremely subtle and the number of samples per class tends to be low, are parti…
Channel-Based Attention for LCC Using Sentinel-2 Time Series
Hermann Courteille, A. Benoît, N Méger +2
Deep Neural Networks (DNNs) are getting increasing attention to deal with Land Cover Classification (LCC) relying on Satellite Image Time Series (SITS). Though high performances ca…
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…
Fine grained classification for multi-source land cover mapping
Yawogan Jean Eudes Gbodjo, Dino Ienco, Louise Leroux +2
Nowadays, there is a general agreement on the need to better characterize agricultural monitoring systems in response to the global changes. Timely and accurate land use/land cover…
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…