We introduce a multi-branch Deep Learning architecture that allows for the extraction of multi-scale features. Exploiting the data multi-modality structure through the combined use of various feature extractors provides high performance on data …
Two oral presentations in IGARSS 2021:
- Convolutional Autoencoder for unsupervised representation learning of PolSAR Time-Series (5 min) (paper) - Multi-Branch Deep Learning model for detection of settlements without electricity (10 min) (paper) …
Temporal Convolutional AutoEncoders are used as feature extractors to project time series onto a latent space where similarity detection can be easily performed. This model can generate accurate descriptors of the temporal profile of the input …