DEEP CLUSTERING METHODS STUDY APPLIED TO SATELLITE IMAGES TIME SERIES

Clustering is an essential tool for data analysis and visualization. It is particularly useful in case of a lack of labels, which prevent the use of supervised methods. The analysis of satellite images is particularly prone to this problem, especially when studied as time series, because the access to this type of data is still recent. Among all clustering methods, the ones based on Deep Neural Networks (DNNs) have seen an increasing interest lately, but only a few works have been conducted on time series yet. This paper aims to give more insight on how current clustering methods based on DNNs can be applied to Satellite Images Time Series (SITS) and it shows that with a proper configuration they can perform better compared to classical non-deep methods.