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Exploring The Fusion Of Sentinel-1 Sar And Sentinel-2 Msi Data For Built-Up Area Mapping Using Deep Learning
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-3560-638x
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-1369-3216
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0001-9692-8636
2021 (English)In: International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc. , 2021, p. 4720-4723Conference paper, Published paper (Refereed)
Abstract [en]

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 multi-spectral 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. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2021. p. 4720-4723
Keywords [en]
Built-up area mapping, Data fusion, Deep learning, Sentinel-1, Sentinel-2
National Category
Physical Geography
Identifiers
URN: urn:nbn:se:kth:diva-316278DOI: 10.1109/IGARSS47720.2021.9553448Scopus ID: 2-s2.0-85126039388OAI: oai:DiVA.org:kth-316278DiVA, id: diva2:1688305
Conference
2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, 12 July 2021 through 16 July 2021
Note

Part of proceedings: ISBN 978-1-6654-0369-6

QC 20220818

Available from: 2022-08-18 Created: 2022-08-18 Last updated: 2024-01-09Bibliographically approved

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Hafner, SebastianBan, YifangNascetti, Andrea

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