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Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture
The huge amount of data currently produced by modern Earth Observation (EO) missions has
allowed for the design of advanced machine learning techniques able to support complex …
allowed for the design of advanced machine learning techniques able to support complex …
Land cover classification via multitemporal spatial data by deep recurrent neural networks
Nowadays, modern earth observation programs produce huge volumes of satellite images
time series that can be useful to monitor geographical areas through time. How to efficiently …
time series that can be useful to monitor geographical areas through time. How to efficiently …
A constrastive semi-supervised deep learning framework for land cover classification of satellite time series with limited labels
In this work, we present a new semi-supervised learning framework to cope with satellite
image time series (SITS) classification in a data paucity scenario, considering extreme low …
image time series (SITS) classification in a data paucity scenario, considering extreme low …
From context to distance: Learning dissimilarity for categorical data clustering
Clustering data described by categorical attributes is a challenging task in data mining
applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values …
applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values …
Context-based distance learning for categorical data clustering
Clustering data described by categorical attributes is a challenging task in data mining
applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values …
applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values …
Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch
Since the BOSS competition, in 2010, most steganalysis approaches use a learning
methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image …
methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image …
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
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 …
A notable example is represented by the Sentinel-2 mission, which provides images at …
Deep semi-supervised clustering for multi-variate time-series
D Ienco, R Interdonato - Neurocomputing, 2023 - Elsevier
Huge amount of data are nowadays produced by a large and disparate family of sensors,
which typically measure multiple variables over time. Such rich information can be profitably …
which typically measure multiple variables over time. Such rich information can be profitably …
Deep multivariate time series embedding clustering via attentive-gated autoencoder
D Ienco, R Interdonato - Advances in Knowledge Discovery and Data …, 2020 - Springer
Nowadays, great quantities of data are produced by a large and diverse family of sensors (eg,
remote sensors, biochemical sensors, wearable devices), which typically measure …
remote sensors, biochemical sensors, wearable devices), which typically measure …
Deep recurrent neural networks for winter vegetation quality mapping via multitemporal SAR Sentinel-1
Mapping winter vegetation quality is a challenging problem in remote sensing. This is due
to cloud coverage in winter periods, leading to a more intensive use of radar rather than …
to cloud coverage in winter periods, leading to a more intensive use of radar rather than …