Learning Disentangled Representations of Satellite Image Time Series
This work addresses the problem of analyzing satellite image time series for applications such as change detection, but it is incremental as it builds on existing VAE and GAN techniques.
The paper tackles unsupervised learning of disentangled representations for satellite image time series by separating shared and exclusive information, using a model combining cross-domain autoencoders with VAE and GAN methods, and demonstrates utility in tasks like classification and change detection with experiments on Sentinel-2 data.
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 ad-versarial 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 from 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.