Abstract
The small Baseline Synthetic Aperture Radar (SAR) Interferometry (SBI) technique has been widely and successfully applied in various ground deformation monitoring applications. Over the last decade, a variety of SBI algorithms have been developed based on the same fundamental concepts. Recently developed SBI toolboxes provide an open environment for researchers to apply different SBI methods for various purposes. However, there has been no thorough discussion that compares the particular characteristics of different SBI methods and their corresponding performance in ground deformation reconstruction. Thus, two SBI toolboxes that implement a total of four SBI algorithms were selected for comparison. This study discusses and summarizes the main differences, pros and cons of these four SBI implementations, which could help users to choose a suitable SBI method for their specific application. The study focuses on exploring the suitability of each SBI module under various data set conditions, including small/large number of interferograms, the presence or absence of larger time gaps, urban/vegetation ground coverage, and temporally regular/irregular ground displacement with multiple spatial scales. Within this paper we discuss the corresponding theoretical background of each SBI method. We present a performance analysis of these SBI modules based on two real data sets characterized by different environmental and surface deformation conditions. The study shows that all four SBI processors are capable of generating similar ground deformation results when the data set has sufficient temporal sampling and a stable ground backscatter mechanism like urban area. Strengths and limitations of different SBI processors were analyzed based on data set configuration and environmental conditions and are summarized in this paper to guide future users of SBI techniques.
Highlights
Introduction and MotivationTime-series (TS) Synthetic Aperture Radar Interferometry (InSAR) analysis is a type of advanced technique developed to overcome limitations of the classical differential InSAR (DInSAR) method, e.g., temporal and geometrical decorrelation, and to compensate error contributions from atmospheric distortions, inaccurate terrain-models and uncertain satellite orbits
To conduct adaptive filtering correctly, statistical tests are applied to find homogenous pixels from amplitude images beforehand [42,43]. This has been implemented in other multi-temporal interferometry techniques (e.g., SqueeSARTM [11,44,45]); it will not be discussed in this paper
These sub-sites are denoted by smaller boxes shown in Figure 2, while the Okmok site was analyzed in one piece
Summary
Time-series (TS) Synthetic Aperture Radar Interferometry (InSAR) analysis is a type of advanced technique developed to overcome limitations of the classical differential InSAR (DInSAR) method, e.g., temporal and geometrical decorrelation, and to compensate error contributions from atmospheric distortions, inaccurate terrain-models and uncertain satellite orbits. A wide range of methods were developed that utilize small baseline differential interferograms for surface deformation estimation. Many approaches in the SBI technique family are applied to multi-looked interferograms to further reduce decorrelation noise [6,23], while other algorithms are able to work with full resolution data [9,24]. Three SBI strategies, including the conventional SBAS [6], NSBAS [7,22,25], and a temporal analysis method (Timefun) adapted from the MInTS algorithm [8], have been implemented in the GIAnT toolbox. We conduct a quantitative analysis that compares the performance of four SBI modules, including the SBI method developed in the StaMPS/MTI toolbox and three SBI approaches implemented in the GIAnT toolbox.
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