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Proceedings Paper

Multitemporal RADARSAT-2 polarimetric SAR data for urban land-cover mapping
Author(s): Liang Gao; Yifang Ban
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Paper Abstract

The objective of this research is to evaluate the performance of multitemporal RADARSAT-2 polarimetric SAR data for urban land use/land-cover classification. Three dates of RADARSAT-2 polarimetric SAR data were acquired during the summer of 2008 over the rural-urban fringe of the Greater Toronto Area. The major land-cover types are residential areas, industry areas, bare land, golf courses, forest, and agricultural crops. The methodology used in this study follow the manner that first extracting the features and then carrying out the supervised classification taking the different feature combinations as an input. Support vectors machine is selected to be the classifier. SAR features including amplitude, intensity, long-term coherence, Freeman-Durden decomposition are extracted and compared by evaluating the classification abilities. Long-term coherence plays an important role in building discrimination in this study. The best classification results achieved by using the three dates HH, VH, HV amplitude layers and the coherence map. The overall accuracy is 82.3%. The results indicate that RADARSAT-2 polarimetric data has a potential to urban land-cover classification with the proper feature combinations.

Paper Details

Date Published: 4 November 2010
PDF: 9 pages
Proc. SPIE 7841, Sixth International Symposium on Digital Earth: Data Processing and Applications, 78410N (4 November 2010); doi: 10.1117/12.873218
Show Author Affiliations
Liang Gao, Royal Institute of Technology (Sweden)
Yifang Ban, Royal Institute of Technology (Sweden)


Published in SPIE Proceedings Vol. 7841:
Sixth International Symposium on Digital Earth: Data Processing and Applications
Huadong Guo; Changlin Wang, Editor(s)

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