Exploring the Fusion of Sentinel-1 SAR and Sentinel-2 MSI Data for Built-Up Area Mapping Using Deep Learning

Abstract

This research explores the potential of combining Sentinel-1 C-band Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) data for Built-Up Area (BUA) mapping using deep learning. A lightweight U-Net model is trained using openly available building footprint reference data in North America and tested in four cities across three additional continents. The best test performance in terms of F1 score was achieved by the joint use of SAR and multispectral data (0.676), followed by multi-spectral (0.611) and SAR data (0.601). The developed fusion approach is particularly promising to distinguish BUA in low-density residential neighborhoods. Furthermore, our fusion approach compares favorably to the state-of-the-art in BUA mapping in the selected cities. However, associated with the diverse characteristics of human settlements around the world, considerable differences in accuracy among the test cities were observed. This indicates the need for more sophisticated fusion techniques to improve CNN model generalization and for adding more diverse training data.