Surface deformation extraction from small baseline subset synthetic aperture radar interferometry (SBAS-InSAR) using coherence-optimized baseline combinations

Abstract

ABSTRACT Surface deformation data can be used to provide early warnings of geohazards and are useful in a variety of research fields. The Small BAseline Subset InSAR (SBAS-InSAR) boosts the data sampling rate and improves the accuracy of deformation extraction by restricting the temporal and spatial baselines. However, various factors, such as the types of ground objects and seasons, affect the coherence between SAR images. Traditional SBAS-InSAR employs a fixed temporal baseline, which does not guarantee good coherence and might lead to decorrelation. In this paper, we propose that instead of using a fixed temporal baseline, we directly use the average coherence between SAR images as the baseline constraint index to perform an optimized selection of SBAS-InSAR interferometric pairs, ensuring good coherence of the interferometric pairs and improving the quality of the interferometric fringes. The proposed approach was used to extract surface deformation in two test experiment areas: Houston and Sydney. Compared with the conventional SBAS-InSAR and GPS data, the standard deviation of error of Houston and Sydney dropped from 0.813 to 0.589 and 0.291 to 0.246, respectively; the root mean square error (RMSE) decreased from 1.082 to 1.041 and 0.485 to 0.334, respectively, indicating that the proposed method has better surface deformation extraction accuracy. After demonstrating the accuracy of the proposed method, it was applied to Pingxiang area, a mining city in China, to effectively extract and analyze the surface deformation induced by mining activities, which proves the universality of this method in different scenarios.