16 citations · 24 across the 6 of their papers we have counts for
8 papers · 1 filter
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…