• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.165-177
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• 2008

Small baseline subset (SBAS) algorithm has been recently developed using an appropriate combination of differential interferograms, which are characterized by a small baseline in order to minimize the spatial decorrelation. This algorithm uses the singular value decomposition (SVD) to measure the time-series surface deformation from the differential interferograms which are not temporally connected. And it mitigates the atmospheric effect in the time-series surface deformation by using spatially low-pass and temporally high-pass filter. Nevertheless, it is not easy to correct the phase unwrapping error of each interferogram and to mitigate the time-varying noise component of the surface deformation from this algorithm due to the assumption of the linear surface deformation in the beginning of the observation. In this paper, we present an improved SBAS technique to complement these problems. Our improved SBAS algorithm uses an iterative approach to minimize the phase unwrapping error of each differential interferogram. This algorithm also uses finite difference method to suppress the time-varying noise component of the surface deformation. We tested our improved SBAS algorithm and evaluated its performance using 26 images of ERS-1/2 data and 21 images of RADARSAT-1 fine beam (F5) data at each different locations. Maximum deformation amount of 40cm in the radar line of sight (LOS) was estimated from ERS-l/2 datasets during about 13 years, whereas 3 cm deformation was estimated from RADARSAT-1 ones during about two years.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.101-115
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• 2018

The Long Valley area and its surroundings are part of a major volcano system where inflation occurred in the resurgent dome in the 1990s. We used ENVISAT data to monitor surface deformation of the Long Valley area and its surroundings after the inflation, from 2003-2010. To retrieve the time series of the deformation, we applied the refined Small BAseline Subset (SBAS) algorithm which is improved using an iterative approach to minimize unwrapping error. Moreover, ascending and descending data were used to decompose the horizontal and vertical deformation in detail. To confirm refined SBAS results, we used GPS dataset. The InSAR errors are estimated as ${\pm}1.0mm/yr$ and ${\pm}0.8mm/yr$ from ascending and descending tracks, respectively. Compare to the previous study of 1990s over the Long Valley and its surroundings, Paoha Island and CASA geothermal area still subside. The deformation pattern in the Long Valley area during the study period (2003-2010) went through both subsidence (2003-2007) and slow uplift(2007-2010) episodes. Our research also shows no deformation signal near McGee Creek. Our study provided a better understanding of the surface changes of the indicators in the 1990s and 2000s.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.143-151
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• 2010

We carried out studies on InSAR techniques for time-series deformation monitoring using multi-temporal SAR. The PSInSAR method using permanent scatterer is much more complicate than the SBAS because it includes many non-linear equation due to the input of wrapped phase. It is conformed the PS algorithm is very sensitive to even PSC selection. On the other hand, the SBAS method using interferogram of small baseline subset is simple but sensitive to the accuracy of unwrapped phase. The SBAS is better method for expecting not significant unwrapping error while PSInSAR is more proper method for expecting local deformation within very limited area. We used 51 ERS-1/2 SAR data during 1992-2000 over Las Vegas, USA for the comparison between PSInSAR and SBAS. Both PSInSAR and SBAS show similar ground deformation value although local deformation seems to be detected in the PSInSAR method only.