A constrained small baseline subsets (CSBAS) InSAR technique for multiple subsets

Liu, Yuanyuan; Zhao, Chaoying; Zhang, Qin; Lü, Zhong; Zhang, Jing

doi:10.1080/22797254.2019.1707715

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…5(a). Fortunately, many methods have been developed on the basis of the SBAS method to perform deformation retrieval for nontime-overlapped subsets [35]- [37]. The non-time-overlapped subsets are expanded into a large subset by connecting an additional pair with relatively high coherence between the different subsets [35], after which the SBAS method is applied.…”

Section: A Terrasar-x Datamentioning

confidence: 99%

A Method for Selecting SAR Interferometric Pairs Based on Coherence Spectral Clustering

Wang,

Xu,

Zeng

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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To achieve accurate interferometric synthetic aperture radar (SAR) phase estimation, it is essential to select appropriate high-coherence interferometric pairs from massive SAR single-look complex (SLC) image data. The selection should include as many high-coherence interferometric pairs as possible while avoiding low-coherence pairs. By combining coherence and spectral clustering, a novel selection method for SAR interferometric pairs is proposed in this paper. The proposed method can be adopted to classify SAR SLC images into different clusters, where the total coherence of interferometric pairs in the same cluster is maximized while that among the different clusters is minimized. This is implemented by averaging the coherence matrices of representative pixels to construct an adjacency matrix and performing eigenvalue decomposition for estimating the number of clusters. The effectiveness of the proposed method is demonstrated using 33 TerraSAR-X and 38 dual-polarization Sentinel-1A data samples, yielding improved topography and deformation monitoring results.

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Section: A Terrasar-x Datamentioning

confidence: 99%

A Method for Selecting SAR Interferometric Pairs Based on Coherence Spectral Clustering

Wang,

Xu,

Zeng

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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“…Additionally, an idea for long-term deformation monitoring has been proposed by Deng et al [18], who proposed a multi-platform long-time-sequence SBAS (MC-SBAS) method. In 2019, Liu et al [19,20] proposed solving the rank deficit problem by incorporating the minimum norm benchmark equation and thus developed the constrained SBAS (CSBAS) method.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Sequential SBAS Processing Framework Based on Hadoop Distributed Computing

Wu,

Lv,

Yun

et al. 2024

Remote Sensing

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With the rapid development of microwave remote sensing and SAR satellite systems, the use of InSAR techniques has been greatly encouraged due to the abundance of SAR data with unprecedented temporal and spatial coverage. Small Baseline Subset (SBAS) is a promising time-series InSAR method for applications involving deformation monitoring of the Earth’s crust, and the sequential SBAS method is an extension of SBAS that allows long-term and large-scale surface displacements to be obtained with continuously auto-updating measurement results. As the Chinese LuTan-1 SAR system has begun acquiring massive SAR image data, the need for an efficient and lightweight InSAR processing platform has become urgent in various research fields. However, traditional sequential algorithms are incapable of meeting the huge challenges of low efficiency and frequent human interaction in large-scale InSAR data processing. Therefore, this study proposes a distributed parallel sequential SBAS (P2SBAS) processing chain based on Hadoop by effectively parallelizing and improving the current sequential SBAS method. P2SBAS mainly consists of two components: (1) a distributed SAR data storage platform based on HDFS, which supports efficient inter-node data transfer and continuous online data acquisition, and (2) several parallel InSAR processing algorithms based on the MapReduce model, including image registration, filtering, phase unwrapping, sequential SBAS processing, and so on. By leveraging the capabilities associated with the distributed nature of the Hadoop platform, these algorithms are able to efficiently utilize the segmentation strategy and perform careful boundary processing. These parallelized InSAR algorithm modules can achieve their goals on different nodes in the Hadoop distributed environment, thereby maximizing computing resources and improving the overall performance while comprehensively considering performance and precision. In addition, P2SBAS provides better computing and storage capabilities for small- and medium-sized teams compared to popular InSAR processing approaches based on cloud computing or supercomputing platforms, and it can be easily deployed on clusters thanks to the integration of various existing computing components. Finally, to demonstrate and evaluate the efficiency and accuracy of P2SBAS, we conducted comparative experiments on a set of 32 TerraSAR images of Beijing, China. The results demonstrate that P2SBAS can fully utilize various computing nodes to improve InSAR processing and can be applied well in large-scale LuTan-1 InSAR applications in the future.

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Review of the SBAS InSAR Time-series algorithms, applications, and challenges

2022

Geodesy and Geodynamics

100

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A constrained small baseline subsets (CSBAS) InSAR technique for multiple subsets

Cited by 6 publications

References 32 publications

A Method for Selecting SAR Interferometric Pairs Based on Coherence Spectral Clustering

A Method for Selecting SAR Interferometric Pairs Based on Coherence Spectral Clustering

A Parallel Sequential SBAS Processing Framework Based on Hadoop Distributed Computing

Review of the SBAS InSAR Time-series algorithms, applications, and challenges

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