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EO4Urban: Sentinel-1A SAR and Sentinel-2A MSI data for global urban services
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
2017 (English)In: 2017 Joint Urban Remote Sensing Event, JURSE 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, 7924550Conference paper, (Refereed)
Abstract [en]

The overall objective of this research is to evaluate multitemporal Sentinel-1A SAR and Sentinel-2A MSI data for global urban services using innovative methods and algorithms, namely KTH-Pavia Urban Extractor, a robust algorithm for urban extent extraction and KTH-SEG, a novel object-based classification method for detailed urban land cover mapping. Ten cities around the world in different geographical and environmental conditions were selected as study areas. Large volumes of Sentinel-1A SAR and Sentinel-2A MSI data were acquired during the vegetation season in 2015 and 2016. The urban extraction results showed that urban areas and small towns could be well extracted using multitemporal Sentinel-1 SAR, Sentinel-2A MSI data and their fusion using the Urban Extractors developed within the project. For urban land cover mapping, multitemporal Sentinel-1A SAR data alone yielded an overall classification accuracy of 60% for Stockholm. Sentinel-2A MSI data as well as the fusion of Sentinel-1A SAR and Sentinel-2A MSI data, however, produced much higher classification accuracies, both reached 80%.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. 7924550
Keyword [en]
EO4Urban, Global Urban Services, KTH-SEG, Sentinel-1A SAR, Sentinel-2A MSI, Urban Extractor
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-209727DOI: 10.1109/JURSE.2017.7924550Scopus ID: 2-s2.0-85020179025ISBN: 9781509058082 (print)OAI: oai:DiVA.org:kth-209727DiVA: diva2:1114801
Conference
2017 Joint Urban Remote Sensing Event, JURSE 2017, Dubai, United Arab Emirates, 6 March 2017 through 8 March 2017
Note

QC 20170626

Available from: 2017-06-26 Created: 2017-06-26 Last updated: 2017-06-26Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
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Output format
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