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ResUCTransNet: An InSAR Phase Unwrapping Network Combining Residual Structure and Channel Transformer.

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Title: ResUCTransNet: An InSAR Phase Unwrapping Network Combining Residual Structure and Channel Transformer.
Authors: Chen, Yuejuan1,2 (AUTHOR), Han, Yu1,2 (AUTHOR), Huang, Pingping1,2 (AUTHOR) hwangpp@imut.edu.cn, Tan, Weixian1,2 (AUTHOR), Wang, Zhiguo1,2 (AUTHOR), Qi, Yaolong1,2 (AUTHOR)
Source: Remote Sensing. Mar2026, Vol. 18 Issue 5, p705. 24p.
Subjects: Phase unwrapping (Digital image processing), Transformer models, Deep learning, Artificial neural networks
Abstract: Highlights: What are the main findings? We developed ResUCTransNet, a deep learning network that integrates residual learning with a channel transformer for InSAR phase unwrapping. In this model, we replace the conventional skip connections in Res_UNet with a channel Transformer composed of channel-wise cross fusion and cross-attention modules. This design mitigates semantic inconsistency between encoder and decoder features. ResUCTransNet maintains low model complexity and effectively reducing unwrapping errors. It also better preserves fringe continuity and terrain structural information. What are the implications of the main findings? Channel-wise cross-scale attention is an effective alternative to skip connections in deep phase unwrapping networks. It alleviates feature mismatch between the encoder and decoder. The proposed method is more robust under dense fringes, low coherence, and strong noise. It provides reliable support for high-precision InSAR deformation monitoring and topographic inversion. Phase unwrapping in interferometric synthetic aperture radar (InSAR) aims to recover a continuous phase field from wrapped observations, which enable accurate topographic reconstruction and surface deformation measurements. With the recent advances in deep learning (DL), several DL-based unwrapping approaches have shown promising performance. However, deep learning networks suffer from inconsistent feature representations between encoder and decoder stages. This leads to incompatible skip connections that provide limited benefits and even degrade reconstruction quality. To overcome this limitation, we propose ResUCTransNet that integrates residual learning with transformer-based feature modeling. The network employs a multi-scale residual backbone derived from Res_UNet to extract stable deep features. Then, to replace conventional skip connections, a channel transformer (CTrans) module is introduced that composed of channel-wise cross fusion transformer (CCT) and channel-wise cross attention (CCA). This design effectively reduces the semantic gap in different network stages, which allows adaptive integration of local CNN features and global transformer representations. Experiments on the public InSAR-DLPU dataset demonstrate that ResUCTransNet effectively reduces model complexity and achieves substantial improvements over existing deep learning models and classical unwrapping algorithms. Specifically, the proposed method attains the best performance in terms of RMSE and SSIM (RMSE = 1.6247, SSIM = 0.7741). Compared with the second-best model, Res_Unet (RMSE = 2.8409, SSIM = 0.7733), ResUCTransNet achieves an approximately 42.8% reduction in RMSE while maintaining nearly identical structural similarity. The proposed method provides higher reconstruction accuracy and better structural fidelity, while maintaining strong robustness and generalization in complex terrain or severe noise 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.)
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  Data: ResUCTransNet: An InSAR Phase Unwrapping Network Combining Residual Structure and Channel Transformer.
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  Data: Highlights: What are the main findings? We developed ResUCTransNet, a deep learning network that integrates residual learning with a channel transformer for InSAR phase unwrapping. In this model, we replace the conventional skip connections in Res_UNet with a channel Transformer composed of channel-wise cross fusion and cross-attention modules. This design mitigates semantic inconsistency between encoder and decoder features. ResUCTransNet maintains low model complexity and effectively reducing unwrapping errors. It also better preserves fringe continuity and terrain structural information. What are the implications of the main findings? Channel-wise cross-scale attention is an effective alternative to skip connections in deep phase unwrapping networks. It alleviates feature mismatch between the encoder and decoder. The proposed method is more robust under dense fringes, low coherence, and strong noise. It provides reliable support for high-precision InSAR deformation monitoring and topographic inversion. Phase unwrapping in interferometric synthetic aperture radar (InSAR) aims to recover a continuous phase field from wrapped observations, which enable accurate topographic reconstruction and surface deformation measurements. With the recent advances in deep learning (DL), several DL-based unwrapping approaches have shown promising performance. However, deep learning networks suffer from inconsistent feature representations between encoder and decoder stages. This leads to incompatible skip connections that provide limited benefits and even degrade reconstruction quality. To overcome this limitation, we propose ResUCTransNet that integrates residual learning with transformer-based feature modeling. The network employs a multi-scale residual backbone derived from Res_UNet to extract stable deep features. Then, to replace conventional skip connections, a channel transformer (CTrans) module is introduced that composed of channel-wise cross fusion transformer (CCT) and channel-wise cross attention (CCA). This design effectively reduces the semantic gap in different network stages, which allows adaptive integration of local CNN features and global transformer representations. Experiments on the public InSAR-DLPU dataset demonstrate that ResUCTransNet effectively reduces model complexity and achieves substantial improvements over existing deep learning models and classical unwrapping algorithms. Specifically, the proposed method attains the best performance in terms of RMSE and SSIM (RMSE = 1.6247, SSIM = 0.7741). Compared with the second-best model, Res_Unet (RMSE = 2.8409, SSIM = 0.7733), ResUCTransNet achieves an approximately 42.8% reduction in RMSE while maintaining nearly identical structural similarity. The proposed method provides higher reconstruction accuracy and better structural fidelity, while maintaining strong robustness and generalization in complex terrain or severe noise 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.)
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        Value: 10.3390/rs18050705
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 705
    Subjects:
      – SubjectFull: Phase unwrapping (Digital image processing)
        Type: general
      – SubjectFull: Transformer models
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
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      – TitleFull: ResUCTransNet: An InSAR Phase Unwrapping Network Combining Residual Structure and Channel Transformer.
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            NameFull: Chen, Yuejuan
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            NameFull: Han, Yu
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              M: 03
              Text: Mar2026
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              Y: 2026
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