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Fusion of multitemporal spaceborne SAR and optical data for urban mapping and urbanization monitoring
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, Geodesy and Geoinformatics. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-4434-7244
2016 (English)In: Remote Sensing and Digital Image Processing, ISSN 1567-3200, p. 107-123Article in journal (Refereed) Published
Abstract [en]

The overall objective of this research is to evaluate multitemporal spaceborne SAR and optical data for urban land cover mapping and urbanization monitoring. Multitemporal Sentinel-1A SAR and historical ERS SAR and ENVISAT ASAR data as well as HJ-1B multispectral data were acquired in Beijing, Chendgdu and Nanchang, China where rapid urbanization has taken place. KTHSEG, a novel object-based classification method is adopted for urban land cover mapping while KTH-Pavia Urban Extractor, a robust algorithm is improved for urban extent extraction and urbanization monitoring. The research demonstrates that, for urban land cover classification, the fusion of multitemporal SAR and optical data is superior to SAR or optical data alone. The second best classification result is achieved using fusion of 4-date SAR and one HJ-1B image. The results indicate that carefully selected multitemporal SAR dataset and its fusion with optical data could produce nearly as good classification accuracy as the whole multitemporal dataset. The results also show that KTH-SEG, the edge-aware region growing and merging segmentation algorithm, is effective for classification of SAR, optical and their fusion. KTH-SEG outperforms eCognition, the commonly used commercial software, for image segmentation and classification of linear features. For Urban extent extraction, single-date and multitemporal SAR data including ERS SAR, ENVISAT ASAR and Sentinel-1A SAR achieved very promising results in all study areas using the improved KTH-Pavia Urban Extractor. The results showed that urban areas as well as small towns and villages could be well extracted using multitemporal Sentinel-1A SAR data while major urban areas could be well extracted using a single-date single-polarization SAR image. The results clearly demonstrate that multitemporal SAR data are cost- and time-effective way for monitoring spatiotemporal patterns of urbanization. © Springer International Publishing AG 2016.

Place, publisher, year, edition, pages
Springer, 2016. p. 107-123
National Category
Geosciences, Multidisciplinary
Identifiers
URN: urn:nbn:se:kth:diva-202191DOI: 10.1007/978-3-319-47037-5_6Scopus ID: 2-s2.0-85009399794OAI: oai:DiVA.org:kth-202191DiVA, id: diva2:1075639
Projects
EO4Urban project funded by the ESA DUE INNOVATOR III program‘Urbanization and Environment/Climate’ project within the ESA/MOST, China’s joint Dragon III program
Note

QC 20170220

Available from: 2017-02-20 Created: 2017-02-20 Last updated: 2017-02-20Bibliographically approved

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Ban, YifangJacob, Alexander

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