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Mapping urban impervious surface with dual-polarimetric SAR data: An improved method
Highlights Dual-polarimetric SAR data were exploited for impervious surfaces mapping. Usage of polarimetric SAR data was superior over single polarization data. Some individual polarimetric features were found to provide negative effect. Approximately 3.5% improvement was achieved using all polarimetric features.
Abstract Synthetic aperture radar (SAR) data can provide complementary information to improve the mapping of urban impervious surfaces. However, most studies have focused on using only single polarization SAR data. This paper presents a comparative study on the combined use of multispectral optical data and dual polarization SAR data to identify urban impervious surfaces. The experimental results using SPOT-5, TerraSAR-X and ALOS PALSAR data were consistent compared with our previous results using single polarization SAR data. The two-fold result showed that polarimetric SAR images were generally superior to single polarization SAR data for extracting impervious surface areas, although not every individual polarimetric feature could provide a positive result for impervious surfaces mapping. Compared with using only optical and SAR data, the separate HH and HV polarization data improved the accuracy of the results. The incorporation of both Entropy and Alpha features also improved the accuracy. However, the HH/HV ratio and the separate use of coherence did not provide positive results. Noticeably, a combination of all of the dual-polarimetric SAR features was capable of obtaining the best accuracy, with an improvement of approximately 3.5% compared with that of only using SPOT-5 images. This result indicates the superiority of dual-polarimetric SAR data over single polarimetric SAR data for the mapping of urban impervious surfaces.
Mapping urban impervious surface with dual-polarimetric SAR data: An improved method
Highlights Dual-polarimetric SAR data were exploited for impervious surfaces mapping. Usage of polarimetric SAR data was superior over single polarization data. Some individual polarimetric features were found to provide negative effect. Approximately 3.5% improvement was achieved using all polarimetric features.
Abstract Synthetic aperture radar (SAR) data can provide complementary information to improve the mapping of urban impervious surfaces. However, most studies have focused on using only single polarization SAR data. This paper presents a comparative study on the combined use of multispectral optical data and dual polarization SAR data to identify urban impervious surfaces. The experimental results using SPOT-5, TerraSAR-X and ALOS PALSAR data were consistent compared with our previous results using single polarization SAR data. The two-fold result showed that polarimetric SAR images were generally superior to single polarization SAR data for extracting impervious surface areas, although not every individual polarimetric feature could provide a positive result for impervious surfaces mapping. Compared with using only optical and SAR data, the separate HH and HV polarization data improved the accuracy of the results. The incorporation of both Entropy and Alpha features also improved the accuracy. However, the HH/HV ratio and the separate use of coherence did not provide positive results. Noticeably, a combination of all of the dual-polarimetric SAR features was capable of obtaining the best accuracy, with an improvement of approximately 3.5% compared with that of only using SPOT-5 images. This result indicates the superiority of dual-polarimetric SAR data over single polarimetric SAR data for the mapping of urban impervious surfaces.
Mapping urban impervious surface with dual-polarimetric SAR data: An improved method
Zhang, Hongsheng (author) / Lin, Hui (author) / Li, Yu (author) / Zhang, Yuanzhi (author) / Fang, Chaoyang (author)
Landscape and Urban Planning ; 151 ; 55-63
2016-03-12
9 pages
Article (Journal)
Electronic Resource
English
Mapping urban impervious surface with dual-polarimetric SAR data: An improved method
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