Learning Disentangled Representations of Satellite Image Time Series
| Autor: | Mathieu Serrurier, Mathias Ortner, Eduardo Hugo Sanchez |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Contextual image classification
business.industry Computer science 020208 electrical & electronic engineering Pattern recognition 02 engineering and technology Image segmentation 010501 environmental sciences 01 natural sciences Autoencoder 0202 electrical engineering electronic engineering information engineering Unsupervised learning Satellite Image Time Series Artificial intelligence business Representation (mathematics) Image retrieval Change detection 0105 earth and related environmental sciences |
| Zdroj: | Machine Learning and Knowledge Discovery in Databases ISBN: 9783030461324 ECML/PKDD (3) |
| DOI: | 10.1007/978-3-030-46133-1_19 |
| Popis: | In this paper, we investigate how to learn a suitable representation of satellite image time series in an unsupervised manner by leveraging large amounts of unlabeled data. Additionally, we aim to disentangle the representation of time series into two representations: a shared representation that captures the common information between the images of a time series and an exclusive representation that contains the specific information of each image of the time series. To address these issues, we propose a model that combines a novel component called cross-domain autoencoders with the variational autoencoder (VAE) and generative adversarial network (GAN) methods. In order to learn disentangled representations of time series, our model learns the multimodal image-to-image translation task. We train our model using satellite image time series provided by the Sentinel-2 mission. Several experiments are carried out to evaluate the obtained representations. We show that these disentangled representations can be very useful to perform multiple tasks such as image classification, image retrieval, image segmentation and change detection. |
| Databáze: | OpenAIRE |
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