Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach

Abstract

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).

Acknowledgements

Financial support for this project was provided through a Natural Sciences and Engineering Research Council of Canada (NSERC) Research Grant and a Swedish National Space Board grant awarded to Dr Yifang Ban. The RADARSAT fine-beam SAR images were provided by the Canadian Space Agency through RADARSAT-1 Data for Research Use Program.