View in EDS HTML Full Text PDF Full Text

An Improved Closed-Form Multi-Baseline Phase Unwrapping Algorithm.

Saved in:
Bibliographic Details
Title: An Improved Closed-Form Multi-Baseline Phase Unwrapping Algorithm.
Authors: Wang, Zhen1 (AUTHOR), Li, Xuemao1,2 (AUTHOR), Xing, Chao1 (AUTHOR) xingchao@xidian.edu.cn, Wang, Zhibin2 (AUTHOR), Liu, Peng1 (AUTHOR), Li, Zhenfang1 (AUTHOR)
Source: Remote Sensing. Jan2026, Vol. 18 Issue 2, p214. 27p.
Subjects: Phase unwrapping (Digital image processing), Algorithms, Signal processing, Statistical reliability, Chinese remainder theorem, Engineering
Abstract: Highlights: What are the main findings? The number of theoretical intercepts and the calculation method for each one are specified precisely. The practical intercept filtering method and the closed-form solution for ambiguity number are presented. What is the implication of the main finding? The process of intercept search and ambiguity number search has been avoided. This has significantly enhanced processing efficiency. Constructing and solving the Chinese Remainder Theorem equations with error-free intercepts as remainders has greatly improved processing accuracy. Multi-baseline phase unwrapping (PU) is an extension of single-baseline PU. Its accuracy directly affects the reliability of results in engineering tasks, such as InSAR topographic mapping and geological hazard monitoring, in complex scenarios. Meanwhile, its efficiency determines the timeliness of data delivery in emergency scenarios. The cluster-analysis (CA)-based algorithm represents a significant advancement in multi-baseline PU algorithms, wherein a strategy for pixel clustering and uniform PU is introduced. However, in the CA algorithm, phase noise degrades pixel clustering performance, leading to deviations in the determination of intercept centerlines and ultimately errors in ambiguity number search. In addition, the computational complexity is increased by the search for intercept peaks and ambiguity numbers. To address these limitations and ensure that accuracy and efficiency requirements are met in practical applications, an improved closed-form multi-baseline PU algorithm is proposed in this article. Compared with conventional CA algorithms, this algorithm offers the following four improvements. First, differential phase processing is introduced into the algorithm, which not only mitigates the impact of phase noise on pixel clustering but also provides new inputs for subsequent ambiguity-number solution. Secondly, a novel method for calculating the theoretical intercept is proposed, which depends solely on the external reference DEM and the ambiguity height. Thirdly, to eliminate the need for peak-intercept search and to suppress error propagation from incorrect intercepts, an intercept filtering method is introduced into the algorithm. In this method, a categorized filtering of actual intercepts for all pixels is performed. Fourthly, to address the phase-noise sensitivity and low efficiency in ambiguity-number search, the algorithm proposes a closed-form ambiguity-number solution method based on the Chinese Remainder Theorem (CRT). In this method, calculation accuracy can be ensured and solution efficiency improved by constructing and solving CRT equation groups with filtered error-free intercepts as remainders. The aforementioned four points are not independent of each other, but are strongly logically dependent and correlated. The effectiveness of the proposed algorithm is validated through one simulated data experiment and two real data experiments. The proposed algorithm achieves improvements in accuracy and efficiency across the three datasets. In terms of accuracy, the RMSE is reduced by at least 11.52%, while the PUSR increases by at least 1.36%. In terms of efficiency, runtime is shortened by at least 29.75%. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 191173499
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: An Improved Closed-Form Multi-Baseline Phase Unwrapping Algorithm.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Zhen%22">Wang, Zhen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xuemao%22">Li, Xuemao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xing%2C+Chao%22">Xing, Chao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xingchao@xidian.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Zhibin%22">Wang, Zhibin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Peng%22">Liu, Peng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zhenfang%22">Li, Zhenfang</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jan2026, Vol. 18 Issue 2, p214. 27p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Phase+unwrapping+%28Digital+image+processing%29%22">Phase unwrapping (Digital image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+reliability%22">Statistical reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Chinese+remainder+theorem%22">Chinese remainder theorem</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering%22">Engineering</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? The number of theoretical intercepts and the calculation method for each one are specified precisely. The practical intercept filtering method and the closed-form solution for ambiguity number are presented. What is the implication of the main finding? The process of intercept search and ambiguity number search has been avoided. This has significantly enhanced processing efficiency. Constructing and solving the Chinese Remainder Theorem equations with error-free intercepts as remainders has greatly improved processing accuracy. Multi-baseline phase unwrapping (PU) is an extension of single-baseline PU. Its accuracy directly affects the reliability of results in engineering tasks, such as InSAR topographic mapping and geological hazard monitoring, in complex scenarios. Meanwhile, its efficiency determines the timeliness of data delivery in emergency scenarios. The cluster-analysis (CA)-based algorithm represents a significant advancement in multi-baseline PU algorithms, wherein a strategy for pixel clustering and uniform PU is introduced. However, in the CA algorithm, phase noise degrades pixel clustering performance, leading to deviations in the determination of intercept centerlines and ultimately errors in ambiguity number search. In addition, the computational complexity is increased by the search for intercept peaks and ambiguity numbers. To address these limitations and ensure that accuracy and efficiency requirements are met in practical applications, an improved closed-form multi-baseline PU algorithm is proposed in this article. Compared with conventional CA algorithms, this algorithm offers the following four improvements. First, differential phase processing is introduced into the algorithm, which not only mitigates the impact of phase noise on pixel clustering but also provides new inputs for subsequent ambiguity-number solution. Secondly, a novel method for calculating the theoretical intercept is proposed, which depends solely on the external reference DEM and the ambiguity height. Thirdly, to eliminate the need for peak-intercept search and to suppress error propagation from incorrect intercepts, an intercept filtering method is introduced into the algorithm. In this method, a categorized filtering of actual intercepts for all pixels is performed. Fourthly, to address the phase-noise sensitivity and low efficiency in ambiguity-number search, the algorithm proposes a closed-form ambiguity-number solution method based on the Chinese Remainder Theorem (CRT). In this method, calculation accuracy can be ensured and solution efficiency improved by constructing and solving CRT equation groups with filtered error-free intercepts as remainders. The aforementioned four points are not independent of each other, but are strongly logically dependent and correlated. The effectiveness of the proposed algorithm is validated through one simulated data experiment and two real data experiments. The proposed algorithm achieves improvements in accuracy and efficiency across the three datasets. In terms of accuracy, the RMSE is reduced by at least 11.52%, while the PUSR increases by at least 1.36%. In terms of efficiency, runtime is shortened by at least 29.75%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=191173499
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18020214
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 214
    Subjects:
      – SubjectFull: Phase unwrapping (Digital image processing)
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Signal processing
        Type: general
      – SubjectFull: Statistical reliability
        Type: general
      – SubjectFull: Chinese remainder theorem
        Type: general
      – SubjectFull: Engineering
        Type: general
    Titles:
      – TitleFull: An Improved Closed-Form Multi-Baseline Phase Unwrapping Algorithm.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Wang, Zhen
      – PersonEntity:
          Name:
            NameFull: Li, Xuemao
      – PersonEntity:
          Name:
            NameFull: Xing, Chao
      – PersonEntity:
          Name:
            NameFull: Wang, Zhibin
      – PersonEntity:
          Name:
            NameFull: Liu, Peng
      – PersonEntity:
          Name:
            NameFull: Li, Zhenfang
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 01
              Text: Jan2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1