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Extraction of landslide information from airborne polarimetric SAR images based on Bayes decision theory |
| WANG Xingling1, HU Deyong2, TANG Hong3, SHU Yang3 |
1. National Disaster Reduction Center of China, Ministry of Civil Affairs, P R China, Beijing 100124, China;
2. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;
3. Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China |
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Abstract The application of the Radar remote sensing data to landslide investigation is of great importance, especially in cloudy and rainy areas. The high-performance airborne synthetic aperture Radar system(HASARS) developed by Institute of Electronics,Chinese Academy of Sciences,is the first home-made system characterized by multi-band and multi-mode,which has the capability of interferometric survey of X band and double antennas as well as polarimetric observation of P band. In this paper, the accuracies of landslide information extraction from polarimetric SAR data using different polarization combinations were investigated to evaluate the technology, methodology and implementation ideas of the landslide applications with the HASARS, and the focuses included two aspects: the methods of information extraction and the ways to select the feature. The results show that, based on Bayes decision theory and using the samples of landslide and non-landslide in the image to analyze and make decision, the method of feature selection could make classification of polarimetric SAR image satisfactorily. Based on the results of feature selection, the authors extracted the landslide regions from SAR images with supervised classification methods, with their accuracies higher than 90%. The airborne SAR system, with high spatial resolution, high precision DEM production and P band polarimetric observations, can obtain the thematic information of landslide surface more flexibly and precisely, and hence it has a broad prospect in the landslide disaster relief applications.
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| Keywords
vegetation-covered area
soil moisture
inversion
water-cloud model
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Issue Date: 28 March 2014
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2014,
Vol. 26
