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Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301). (Geoinformatik)
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
2010 (English)In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 31, no 6, 1391-1410 p.Article in journal (Refereed) Published
Abstract [en]

The objective of this research is to evaluate Quickbird multi-spectral (MS) data, multi-temporal RADARSAT Fine-Beam C-HH synthetic aperture radar (SAR) data and fusion of Quickbird MS and RADARSAT SAR for urban land-use/land-cover mapping. One scene of Quickbird multi-spectral imagery was acquired on 18 July 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August 2002. Quickbird MS images and RADARSAT SAR data were classified using an object-based and rule-based approach. The results demonstrated that the object-based and knowledge-based approach was effective in extracting urban land-cover classes. For identifying 16 land-cover classes, object-based and rule-based classification of Quickbird MS data yielded an overall classification accuracy of 87.9% (kappa: 0.868). For identifying 11 land-cover classes, object-based and rule-based classification of RADARSAT SAR data yielded an overall accuracy: 86.6% (kappa: 0.852). Decision level fusion of Quickbird classification and RADARSAT SAR classification was able to take advantage of the best classifications of both optical and SAR data, thus significantly improving the classification accuracies of several land-cover classes (25% for pasture, 19% for soybeans, 17% for rapeseeds) even though the overall classification accuracy of 16 land-cover classes increased only slightly to 89.5% (kappa: 0.885).

Place, publisher, year, edition, pages
Taylor & Francis, 2010. Vol. 31, no 6, 1391-1410 p.
Keyword [en]
Fusion, QuickBird, RADARSAT SAR, Urban Land Cover, Object-based, Knowledge-Based
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-27908DOI: 10.1080/01431160903475415ISI: 000277389100003Scopus ID: 2-s2.0-77951132822OAI: oai:DiVA.org:kth-27908DiVA: diva2:382921
Funder
Swedish National Space Board
Note

QC 20110103

Available from: 2011-01-03 Created: 2011-01-03 Last updated: 2017-12-11Bibliographically approved

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