Institution
Geophysical Survey
Facility•Obninsk, Russia•
About: Geophysical Survey is a facility organization based out in Obninsk, Russia. It is known for research contribution in the topics: Geology & Fault (geology). The organization has 308 authors who have published 256 publications receiving 3067 citations. The organization is also known as: Federal State Institution of Science Geophysical Survey of the Siberian Branch of the Russian Academy of Sciences.
Topics: Geology, Fault (geology), Volcano, Signal, Seismic tomography
Papers published on a yearly basis
Papers
More filters
••
••
14 Oct 2010TL;DR: Wang et al. as discussed by the authors used COSMO-SkyMed SAR data and grid digital elevation model ASTER GDEM (30m resolution) for land subsidence mapping of Tianjin and Beijing area.
Abstract: DInSAR can map ground deformation phenomena over tens-of-kilometers-wide area with centimeter-scale accuracy level, and has been considered as a powerful tool. Because of the data availability, previous DInSAR applications were mainly base on ERS, JERS, ENVISAT ASAR, and RADARSAT-1 data with resolution coarser than 10m. Nowadays, several kinds of high resolution SAR data are available for DInSAR and there are high expectations for the application of these data. In this paper, COSMO-SkyMed SAR data and grid digital elevation model ASTER GDEM (30m resolution) are used as research data. COSMO-SkyMed SAR data are preliminary processed by DInSAR technique for land subsidence mapping of Tianjin and Beijing area. Meanwhile, the DInSAR results from COSMO-SkyMed and ASAR data are preliminary compared. COSMO images are more suitable to extract high-precision ground deformation information than ASAR images in civil application given precise orbit data. While COSMO data are suitable for deformation monitoring in a relatively small and important area, ASAR data are fit for regional survey.
••
••
••
TL;DR: In this paper, the authors proposed a new technique for the phase unwrapping of corner reflectors, which can compensate for the limitation of the classical DInSAR, but it requires the observed area to be correlated, and coherence degradation will severely affect the quality of interferogram.
Abstract: Difference interferometric Synthetic aperture radar (DInSAR) has turned out to be a very powerful technique for the
measurement of land deformations, but it requires the observed area to be correlated, and coherence degradation will
seriously affect the quality of interferogram. Corner reflector DInSAR (CRDInSAR) is a new technique in recently years,
which can compensate for the limitation of the classical DInSAR. Due to the stable amplitude and phase performance of
the reflector, the interferometric phase difference of the reflector can be used to monitor or measure the small and slowly
ground deformation for the cases of large geometrical baseline and large time interval between acquisitions. Phase
unwrapping is the process where the absolute phase is reconstructed from its principal value as accurately as possible. It
is a key step in the analysis of DInSAR. The classical phase unwrapping methods are either of path following type or of
minimum-norm type. However, if the coherence of the two images is very low, the both methods will get error result. In
application of CRDInSAR, due to the scattered points, the phase unwrapping of corner reflectors is only dealt with on a
sparse grid, so all the reflectors are connected with Delaunay triangulation firstly, which can be used to define
neighboring points and elementary cycles. When the monitoring ground deformation is slow, that is unwrapped
neighboring-CR phase gradients are supposed to equal their wrapped-phase counterparts, then path-following method
and Phase unwrapping using Coefficient of Elevation-Phase-Relation can be used to phase unwrapping. However, in the
cases of unwrapped gradients exceeding one-half cycle, minimum cost flow (MCF) method can be used to unwrap the interferogram.
Authors
Showing all 331 results
Name | H-index | Papers | Citations |
---|---|---|---|
Imad L. Al-Qadi | 50 | 556 | 10075 |
Griša Močnik | 32 | 105 | 3174 |
Xiang-Yang Li | 32 | 340 | 3849 |
Zhen Leng | 31 | 119 | 2485 |
Wei Xie | 16 | 63 | 875 |
Sergey Senyukov | 15 | 47 | 702 |
Grigory M. Steblov | 14 | 53 | 937 |
Mladen Živčić | 12 | 43 | 778 |
Roger Roberts | 12 | 18 | 379 |
Henning F. Harmuth | 9 | 10 | 312 |
S. Ya. Droznina | 8 | 16 | 221 |
Sergey Khomutov | 8 | 26 | 145 |
Yu. A. Kugaenko | 7 | 23 | 192 |
Jinghui Fan | 7 | 35 | 177 |
S. Droznina | 6 | 9 | 94 |