Published September 14, 2022 | Version v1
Conference paper Open

BiasUNet: Learning Change Detection over Sentinel-2 Image Pairs

  • 1. Information Technologies Institute Centre of Research & Technology - Hellas (CERTH)
  • 2. Reykjavik University


The availability of satellite images has increased due to the fast development of remote sensing technology. As a result several deep
learning change detection methods have been developed to capture spatial changes from multi temporal satellite images that are of great importance in remote sensing, monitoring environmental changes and land use. Recently, a supervised deep learning network called FresUNet has been proposed, which performs a pixel-level change detection from image pairs. In this paper, we extend this method by inserting a Bayesian framework that uses Monte Carlo Dropout, motivated by a recent work in image segmentation. The proposed Bayesian FresUNet (BiasUNet) approach is shown to outperform four state-of-the-art deep learning networks on Sentinel-2 ONERA Satellite Change Detection (OSCD) benchmark dataset, both in terms of precision and quality.



Files (3.7 MB)

Name Size Download all
3.7 MB Preview Download

Additional details


CALLISTO – Copernicus Artificial Intelligence Services and data fusion with other distributed data sources and processing at the edge to support DIAS and HPC infrastructures 101004152
European Commission