Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.contributor.advisor | Zhang, Lei (COMP) | en_US |
| dc.contributor.advisor | Ding, Xiaoli (LSGI) | en_US |
| dc.creator | Liu, Hongyu | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13401 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Error analysis of InSAR models and parameter estimation with spatial constraints | en_US |
| dcterms.abstract | Interferometric Synthetic Aperture Radar (InSAR) is a powerful geodetic technique for identifying surface movements. By interfering with two SAR acquisitions, InSAR can retrieve wide coverage and fine resolution deformation under all weather day and night working conditions. The accuracy of deformation retrieved by conventional differential InSAR (DInSAR) is typically limited due to its inadequate modeling of multiple errors such as atmospheric delay, orbit error, and decorrelation noise. To mitigate their impacts, time series analysis techniques operating on a stack of datasets have been developed for accurate deformation retrieval. Despite abundant research on the properties of primary errors have been conducted, how and to what extent they can affect parameter retrieval are still not quantitatively clear. In addition, although various multi-temporal InSAR (MTInSAR) estimation models have been proposed, they are not unified in a general observation frame. The theoretical relations among these models are not adequately illustrated. Furthermore, since the comprehensive MTInSAR model is rank-deficient, to make the underdetermined system uniquely solvable, time series methods usually have to employ prior constraints. However, the underlying assumptions are sometimes incompatible in actual scenarios. All these issues will degrade the accuracy of the retrieved deformation. | en_US |
| dcterms.abstract | Motivated by limitations in the existing MTInSAR models, the thesis aims to develop innovative error analysis methods and parameter estimators. Firstly, we investigate the characteristics of primary errors through simulated experiments. By manipulating simulation parameters, we reveal their spatial and temporal features in various scenarios and thereby present insights for reasonably eliminating these errors. Moreover, the simulation tests offer us guidance to validate new algorithms with different objectives. | en_US |
| dcterms.abstract | Secondly, starting from the original phase observations (i.e., the un-differenced (UD)), this thesis strictly derives the function and stochastic models for both InSAR single-differenced (SD) and double-differenced (DD) models. Compared with conventional DInSAR, MTInSAR techniques account for multiple errors within a comprehensive observation model. Based on the generic framework, this study exploits the developments of InSAR techniques and relations among representative approaches from a mathematical aspect. As SD and DD models are comparable to the GNSS observation system, this thesis employs the extensively developed GNSS estimation theory to enhance the understanding of InSAR deformation estimation. | en_US |
| dcterms.abstract | Thirdly, using the biased estimation theory, this thesis deeply explores the impacts of systematic errors on the estimation. The estimation biases introduced by unmodeled and baseline errors are quantitatively evaluated through rigorously derived formulas and simulated tests. The results reveal that estimation errors induced by topographic residual can reach to meters level, whereas biases from baseline errors can be safely neglected. This investigation is expected to be useful for optimizing time series models and also highlights the significance of model selection. | en_US |
| dcterms.abstract | Finally, this thesis proposes a spatially constrained method to recover surface deformation within an underdetermined time series InSAR system. To overcome shortcomings of general methods (e.g., temporal deformation model or mathematical constraints), spatial constraints are introduced to the InSAR framework. The underlying rationale is that spatially closer points share more similar deformation patterns, which is more reliable for retrieval of deformation that owns complicated temporal behavior. The proposed method shows its outperformance for recovering the time series deformation with respect to the Moor-Penrose pseudoinverse and StaMPS methods. Additionally, studies of the deformation detection with real data, i.e., Salt Lake in Qinghai-Tibet Plateau and Longyao ground fissure, are presented to validate the obtained results. | en_US |
| dcterms.extent | viii, 128 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Synthetic aperture radar | en_US |
| dcterms.LCSH | Space interferometry | en_US |
| dcterms.LCSH | Error analysis (Mathematics) | en_US |
| dcterms.LCSH | Geodesy | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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