Change detection in Sentinel-2 images using deep learning ensembles

The recent advancements in satellite imaging bring various possibilities in Earth observation in numerous domains, including the analysis of the evolution of urban areas, precision agriculture, environmental monitoring, event detection and tracking, and many more. Change detection plays a key role in a multitude of applications, as it allows for precisely monitoring the changes within an area of interest. In this article, we tackle this issue and introduce deep learning ensembles for change detection in Sentinel-2 times series of multispectral images—the proposed ensembles benefit from different deep learning model architectures. The experimental study performed over the widely-adopted benchmark datasets showed that the ensembles combine the strengths of the individual models, thus they reduce false positives and false negatives of base learners. The ensembles compensated the under-performing models, ultimately obtaining the change detection accuracy that exceeds 95% over the unseen test scenes.