R
Raffaele Gaetano
Researcher at Télécom ParisTech
Publications - 75
Citations - 1528
Raffaele Gaetano is an academic researcher from Télécom ParisTech. The author has contributed to research in topics: Image segmentation & Scale-space segmentation. The author has an hindex of 17, co-authored 69 publications receiving 1054 citations. Previous affiliations of Raffaele Gaetano include Information Technology University & University of Naples Federico II.
Papers
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Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks
TL;DR: RNNs are competitive compared with the state-of-the-art classifiers, and may outperform classical approaches in the presence of low represented and/or highly mixed classes, and it is shown that the alternative feature representation generated by LSTM can improve the performances of standard classifiers.
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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
TL;DR: A deep learning architecture to combine information coming from S1 and S2 time series, namely TWINNS (TWIn Neural Networks for Sentinel data), able to discover spatial and temporal dependencies in both types of SITS is proposed.
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A CNN-Based Fusion Method for Feature Extraction from Sentinel Data
TL;DR: This work proposes to estimate missing optical features through data fusion and deep-learning, and results are very promising, showing a significant gain over baseline methods according to all performance indicators.
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Marker-Controlled Watershed-Based Segmentation of Multiresolution Remote Sensing Images
TL;DR: Numerical results on object layer extraction and simple classification tasks prove the proposed techniques to provide accurate segmentation maps, which preserve fine details and, contrary to state-of-the-art products, can single out objects equally well at very different scales.
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DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
TL;DR: In this paper, the authors proposed the first deep learning architecture for the analysis of Satellite Image Time Series (SITS) data, which combines CNNs and RNNs to exploit complementary information: spatial autocorrelation and temporal dependencies.