View in EDS HTML Full Text PDF Full Text

MultTransNet: A Novel Multimodal Transformer Network for Retrieving Significant Wave Height Using GNSS-R Data.

Saved in:
Bibliographic Details
Title: MultTransNet: A Novel Multimodal Transformer Network for Retrieving Significant Wave Height Using GNSS-R Data.
Authors: Cui, Yinghua1 (AUTHOR) cyh@bistu.edu.cn, Cai, Min1 (AUTHOR), Du, Yuxuan1 (AUTHOR), He, Shanbao1 (AUTHOR)
Source: Remote Sensing. May2026, Vol. 18 Issue 9, p1351. 21p.
Subjects: Feature selection, Boosting algorithms, Marine ecology, Remote sensing, Deep learning
Abstract: Highlights: What are the main findings? The proposed MultTransNet effectively fuses 2D Delay–Doppler Map (DDM) images with 1D auxiliary parameters, reducing Root Mean Square Error (RMSE) by 27.05% and increasing the correlation coefficient (CC) by 7.21% compared with single-modality models. An XGBoost-based iterative feature selection method optimizes the input predictors from 20 to 11 key variables, significantly improving the accuracy of significant wave height (SWH) retrieval, particularly under complex sea-state conditions. What are the implications of the main findings? The fusion of spatial image features and numerical parameters overcomes the limitations of single-modality GNSS-R observations and enhances model robustness in complex marine environments. The improved accuracy and global consistency of the proposed approach enable more reliable all-weather SWH retrieval, supporting marine forecasting, maritime safety, and disaster mitigation. Significant Wave Height (SWH) is a critical parameter for ocean observation. SWH retrieval using GNSS-R data faces challenges including difficult feature selection, insufficient temporal dependency modeling, and limitations due to single-modality data. This paper proposes a novel Multimodal Transformer Network (MultTransNet) to enhance the accuracy of GNSS-R SWH retrieval. To optimize the feature set, we designed an XGBoost-based iterative feature selection module that effectively eliminates redundant features. To capture complex temporal dependencies and global context, the model employs a Transformer encoder utilizing its self-attention mechanism. Furthermore, to overcome the constraints of single-modality data, we innovatively fused 2D DDM image data with 1D auxiliary parameters, enabling multi-source information integration. Simulation results show that the Transformer architecture reduces Root Mean Square Error (RMSE) by 8.91% and increases Correlation Coefficient (CC) by 4.05% compared to a conventional Deep Neural Network (DNN) model. More significantly, the proposed multimodal algorithm further improves retrieval accuracy by 27.05% (RMSE reduction) and 7.21% (CC increase) compared to its single-modality Transformer counterpart, demonstrating superior performance, especially in complex sea-state conditions. [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: 193715382
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: MultTransNet: A Novel Multimodal Transformer Network for Retrieving Significant Wave Height Using GNSS-R Data.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Cui%2C+Yinghua%22">Cui, Yinghua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cyh@bistu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cai%2C+Min%22">Cai, Min</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Du%2C+Yuxuan%22">Du, Yuxuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Shanbao%22">He, Shanbao</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1351. 21p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Marine+ecology%22">Marine ecology</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? The proposed MultTransNet effectively fuses 2D Delay–Doppler Map (DDM) images with 1D auxiliary parameters, reducing Root Mean Square Error (RMSE) by 27.05% and increasing the correlation coefficient (CC) by 7.21% compared with single-modality models. An XGBoost-based iterative feature selection method optimizes the input predictors from 20 to 11 key variables, significantly improving the accuracy of significant wave height (SWH) retrieval, particularly under complex sea-state conditions. What are the implications of the main findings? The fusion of spatial image features and numerical parameters overcomes the limitations of single-modality GNSS-R observations and enhances model robustness in complex marine environments. The improved accuracy and global consistency of the proposed approach enable more reliable all-weather SWH retrieval, supporting marine forecasting, maritime safety, and disaster mitigation. Significant Wave Height (SWH) is a critical parameter for ocean observation. SWH retrieval using GNSS-R data faces challenges including difficult feature selection, insufficient temporal dependency modeling, and limitations due to single-modality data. This paper proposes a novel Multimodal Transformer Network (MultTransNet) to enhance the accuracy of GNSS-R SWH retrieval. To optimize the feature set, we designed an XGBoost-based iterative feature selection module that effectively eliminates redundant features. To capture complex temporal dependencies and global context, the model employs a Transformer encoder utilizing its self-attention mechanism. Furthermore, to overcome the constraints of single-modality data, we innovatively fused 2D DDM image data with 1D auxiliary parameters, enabling multi-source information integration. Simulation results show that the Transformer architecture reduces Root Mean Square Error (RMSE) by 8.91% and increases Correlation Coefficient (CC) by 4.05% compared to a conventional Deep Neural Network (DNN) model. More significantly, the proposed multimodal algorithm further improves retrieval accuracy by 27.05% (RMSE reduction) and 7.21% (CC increase) compared to its single-modality Transformer counterpart, demonstrating superior performance, especially in complex sea-state conditions. [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=193715382
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18091351
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 1351
    Subjects:
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Boosting algorithms
        Type: general
      – SubjectFull: Marine ecology
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Deep learning
        Type: general
    Titles:
      – TitleFull: MultTransNet: A Novel Multimodal Transformer Network for Retrieving Significant Wave Height Using GNSS-R Data.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Cui, Yinghua
      – PersonEntity:
          Name:
            NameFull: Cai, Min
      – PersonEntity:
          Name:
            NameFull: Du, Yuxuan
      – PersonEntity:
          Name:
            NameFull: He, Shanbao
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1