A constrained small baseline subsets (CSBAS) InSAR technique for multiple subsets. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Liu, Yuanyuan; Zhao, Chaoying; Zhang, Qin; Lu, Zhong; Zhang, Jing
• Is Part Of: European journal of remote sensing. Volume 53:Issue 1(2020)
• Page Start: 14
• Page End: 26
• Publication Date: 2020-01-01
• Subjects: Interferometirc Synthetic Aperture Radar (InSAR) -- small baseline subsets (SBAS) -- time series deformation -- frequency-spectrum analysis -- constrained SBAS (CSBAS); Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2019.1707715
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: During the Small Baseline Subsets (SBAS) InSAR processing, the interferograms are often separated into multiple independent subsets, which results inevitably in a rank deficiency problem. Singular value decomposition (SVD) or linear interpolation based least squares (LS) is generally adopted, which causes systematically biased deformation estimation. Therefore, we presents a constrained SBAS method, which takes the period of the time series deformation detected by frequency-spectrum analysis as constraints to solve the rank deficiency problem. This method is illustrated with simulated data in detail. The results of the SVD, LS and our methods are in agreement with the true value in the first subset, but biased in the second subset with the magnitudes of 17.39 cm, 15.90 cm and -0.53 cm, respectively, where our method is the best one. Lastly, the new method is successfully verified using the real SAR data over Southern California from 2003 to 2006. The averaged STD of the differences between our method and GPS observations in four stations are 5.0, 3.62, 6.31 and 5.87 mm/year, respectively, which is much better than those from SVD and LS methods. This outcome confirms the validity of the newly proposed method.