Joint Deep Missing Value Imputation and Clustering of Satellite Image Time Series

Unsupervised classification of satellite image time series (SITS) is prominent in numerous multitemporal remote sensing applications. However, when optical images are concerned, a missing value reconstruction task becomes pivotal, due to the impact of cloud cover on the input SITS. This task is usually addressed as a pre-processing step before feeding the time series to a clustering model. In this paper, we propose a pixel-wise SITS clustering algorithm which integrates missing value imputation jointly with the clustering task. Moreover, the clustering process is designed to jointly perform both representation learning and cluster assignment, enabling the proposed model to simultaneously tackle three duties (missing value imputation, representation learning, and cluster assignment), while being trained in an end-to-end manner. The experimental results show that the proposed model performs well compared to other models that address time series imputation and clustering independently. Furthermore, a visualization analysis suggests that the proposed model learns both the imputation and clustering effectively despite being trained simultaneously.