Detecting Land Cover Changes between Satellite Image Time Series by Exploiting Self-Supervised Representation Learning Capabilities

This work studies change detection from satellite image time series (SITS) with a proposed framework that leverages SITS using self-supervised learning. Experimental evaluation conducted on a study area in southwestern France demonstrates the effectiveness of the approach, with varying quantities of labeled training data. The results highlight the potential of self-supervised learning in producing accurate change detection maps for land cover analysis.