Skip to Main content Skip to Navigation
Conference papers

Deep Temporal Convolutional Autoencoder for Unsupervised Representation Learning of Incoherent Polsar Time-Series

Abstract : 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 time-series. We apply this algorithm to PolSAR S1 uncoherent SAR time series where the model learns highly discriminative data representations. This reduction method is compared to others such as PCA or Temporal Averaging and is shown to outperform them when leveraging the learnt representation using K-Means clustering.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03345533
Contributor : Christine Grec Connect in order to contact the contributor
Submitted on : Wednesday, September 15, 2021 - 4:06:15 PM
Last modification on : Monday, December 13, 2021 - 9:17:24 AM
Long-term archiving on: : Thursday, December 16, 2021 - 7:10:30 PM

File

DTIS21173.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03345533, version 1

Citation

Thomas Martino, Regis Guinvarc'H, Laetitia Thirion-Lefevre, Elise Koeniguer. Deep Temporal Convolutional Autoencoder for Unsupervised Representation Learning of Incoherent Polsar Time-Series. IGARSS 2021, Jul 2021, Bruxelles (virtual), Belgium. ⟨hal-03345533⟩

Share

Metrics

Les métriques sont temporairement indisponibles