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Experimental study of atmospheric phase correction on InSAR with high-resolution DEM |
| LI Man1, XIA Ye1,2, GE Daqing1, ZHANG Ling1, FAN Jinghui1, WANG Yan1 |
1. China Aero Geophysical Survey and Remote Sensing for Land and Resources, Beijing 100083, China;
2. GeoForschungsZentrum, Potsdam 14473, Germany |
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Abstract The most important limiting factor is probably the strong atmospheric wet delay in the application of InSAR to monitoring those landslides located in special location complex topography and rainy climatic condition areas, such as Shuping landslide in Three Gorges Area in China. The atmospheric delay phase could even cover its deformation phase in the area of Shuping landslide at times. To overcome this default of D-InSAR, the authors carried out a correction by the correlation between wet delay phase and elevation, which probably is a considerable atmospheric correction method. Therefore, based on space partial correlation, this paper established the best-fit-function model between wet delay phase and corresponding DEM in the area of Shuping landslide. In fact, wet delay is closely related not only to the elevation but also to the distance along the radar in some cases. In Fig.1 the function of 'Ⅰ’ zone depends on the elevation, while that of 'Ⅱ’ zone is a function of both the elevation and the X (range direction). In view of the least-square method, two best-fit-function models would be found under the condition of the minimum mean square deviation without obvious variation. Finally, the simulated atmospheric delay phase formed by optimal correction models with elevation or/and distance values is removed from the unwrapping phase. And it clearly gives the result of the location, the size and the distribution of the Shuping deformation field within the 11 days. In conclusion, this method could effectively reduce the wet delay phase of the interferogram and is of significance for monitoring the slow deformation of landslides in the Three Gorges Area.
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| Keywords
IKONOS remote sensing data
information extraction
spectral features
density segmentation
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Issue Date: 28 April 2013
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2013,
Vol. 25
