Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Improvement of Small Baseline Subset (SBAS) Algorithm for Measuring Time-series Surface Deformations from Differential SAR Interferograms

차분 간섭도로부터 지표변위의 시계열 관측을 위한 개선된 Small Baseline Subset (SBAS) 알고리즘

  • Jung, Hyung-Sup (Department of Earth System Sciences, Yonsei University) ;
  • Lee, Chang-Wook (Department of Earth System Sciences, Yonsei University) ;
  • Park, Jung-Won (Department of Earth System Sciences, Yonsei University) ;
  • Kim, Ki-Dong (Department of Earth System Sciences, Yonsei University) ;
  • Won, Joong-Sun (Department of Earth System Sciences, Yonsei University)
  • 정형섭 (연세대학교 이과대학 지구시스템과학과) ;
  • 이창욱 (연세대학교 이과대학 지구시스템과학과) ;
  • 박정원 (연세대학교 이과대학 지구시스템과학과) ;
  • 김기동 (연세대학교 이과대학 지구시스템과학과) ;
  • 원중선 (연세대학교 이과대학 지구시스템과학과)
  • Published : 2008.04.30
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Abstract

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.

최근 spatial decorrelation을 극복하기 위하여 비교적 짧은 기선(baseline)을 지니는 여러 시기의 차분간섭도(differential interferogram)로부터 시계열 지표변위(time-series surface deformation)를 관측할 수 있는 small baseline subset(SBAS) 알고리즘이 개발되었다. 이 SBAS 알고리즘은 singluar value decomposition(SVD)을 이용하여 시간별로 완벽하게 연결되지 않는 차분간섭도로부터 시계열 지표변위를 관측하였을 뿐 아니라 공간적으로 저주파 필터와 시간적으로 고주파 필터를 이용하여 대기효과를 보정하였다. 그러나 이 알고리즘은 초기 관측시 시계열 지표변위를 선형으로 가정하였기 때문에 각 차분간섭도의 phase unwrapping 오차를 정확하게 보정하기 어려웠을 뿐 아니라 시계열의 지표변위에 존재하는 노이즈 성분을 완화시키지 못했다. 이와 같은 단점을 보완하기 위하여 이 연구에서는 기존의 SBAS 알고리즘을 개선하였다. 이 개선된 SBAS 알고리즘은 각 차분간섭도의 phase unwrapping 오차를 최소화하기 위하여 반복적으로 시계열 지표면 변위를 개선하였고, 시계열 지표변위의 노이즈를 제거하기 위하여 유한차분근사법(finite difference approximation)을 이용하였다. 서로 다른 지역의 26개의 ERS-12자료와 21개의 RADARSAT-1 fine beam (F5) 자료를 이용하여 개선된 SBAS 알고리즘을 실험하고 분석하였다. ERS-1/2자료에서는 LOS(line-of-sight) 지표변위가 약 13년 동안 최대 -40cm가 관측되었고, RADARSAT-1 fine beam 자료에서는 약 2년 동안 최대 -3cm의 LOS 지표변위가 관측되었다.

Keywords

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