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DIGSFNet: Deformation-Integrity-Guided Symmetric Fusion Network for High-Risk Landslide Extraction from Multi-Source Remote Sensing Images.

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Title: DIGSFNet: Deformation-Integrity-Guided Symmetric Fusion Network for High-Risk Landslide Extraction from Multi-Source Remote Sensing Images.
Authors: Ni, Zixuan1 (AUTHOR), Hu, Lieyun1,2 (AUTHOR), Wang, Huini2,3 (AUTHOR), Li, Fengxiaoxiao3,4 (AUTHOR), Tang, Meng1,4 (AUTHOR), Ma, Guorui1,2 (AUTHOR), Sui, Haigang1,3 (AUTHOR) haigang_sui@263.net
Source: Remote Sensing. Aug2026, Vol. 18 Issue 16, p2692. 32p.
Subjects: Radar interferometry, Landslide hazard analysis, Image fusion, Remote-sensing images, Deep learning
Geographic Terms: Nanning (China)
Abstract: Highlights: What are the main findings? DIGSFNet treats InSAR deformation data and optical imagery as equal information sources through symmetric, dynamically sparse cross-modal fusion. When trained separately on the Nanning-HHLS dataset and the public HAEFNet benchmark, it achieved mIoU values of 83.57 percent and 78.92 percent, outperforming the strongest comparison methods by 2.63 and 3.68 percentage points, respectively. The deformation-integrity decoder incorporates deformation continuity and boundary-gradient consistency into the segmentation process, producing more complete and physically consistent landslide masks and improving the Boundary F1 score by 5.52 percentage points over symmetric fusion alone. What are the implications of the main findings? Explicitly embedding InSAR deformation physics improves the reliability and interpretability of high-risk landslide mapping by reducing missed landslides and helping suppress false alarms caused by non-landslide deformation. The INT8 lightweight student model achieves 79.24 percent mIoU at 218 FPS, with a latency of 4.6 ms and GPU memory consumption of only 0.54 GB, supporting efficient operational surveys over large mountainous areas. High-risk landslide extraction from remote sensing imagery is a fundamental task for geological disaster prevention, emergency response, and land-use planning in mountainous regions. Although deep-learning semantic segmentation has substantially advanced landslide detection from optical imagery, existing methods still suffer from three critical limitations: (i) Interferometric Synthetic Aperture Radar (InSAR) deformation data are treated as auxiliary channels and dominated by optical features during fusion; (ii) predicted masks exhibit fragmented boundaries and incomplete delineation due to the absence of deformation continuity constraints reflecting the physical coherence of slope movements; and (iii) heavy Transformer backbones hinder practical deployment over large areas. To address these issues, we propose a Deformation-Integrity-Guided Symmetric Fusion Network (DIGSFNet) for high-risk landslide extraction from InSAR and optical imagery. The framework consists of three components: a Symmetric Deformation-Aware Encoder (SDAE) that treats InSAR and optical modalities as equal information sources through modality-aware adapters and dynamic sparse cross-modal fusion; a Deformation Integrity Prior Decoder (DIPD) that imposes deformation continuity and boundary-gradient consistency as physical priors to enforce mask completeness and boundary accuracy; and a Lightweight Deployable Student Network (LDSN) obtained via cross-modal knowledge distillation and INT8 quantization for efficient inference. Experiments on the Nanning High-Hazard Landslide Segmentation (Nanning-HHLS) dataset and the public HAEFNet benchmark covering the Qinghai–Tibet–Sichuan landslide-prone regions show that the full DIGSFNet achieves state-of-the-art extraction accuracy, reaching 83.57% and 78.92% mIoU on the two datasets and surpassing the strongest competing method by 2.63 and 3.68 percentage points with a Recall of 91.48% on Nanning-HHLS, while its distilled lightweight student retains 79.24% mIoU at 218 frames per second after INT8 quantization, enabling efficient large-area operational deployment. [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: DIGSFNet: Deformation-Integrity-Guided Symmetric Fusion Network for High-Risk Landslide Extraction from Multi-Source Remote Sensing Images.
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  Data: <searchLink fieldCode="DE" term="%22Radar+interferometry%22">Radar interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Landslide+hazard+analysis%22">Landslide hazard analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Image+fusion%22">Image fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
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  Data: Highlights: What are the main findings? DIGSFNet treats InSAR deformation data and optical imagery as equal information sources through symmetric, dynamically sparse cross-modal fusion. When trained separately on the Nanning-HHLS dataset and the public HAEFNet benchmark, it achieved mIoU values of 83.57 percent and 78.92 percent, outperforming the strongest comparison methods by 2.63 and 3.68 percentage points, respectively. The deformation-integrity decoder incorporates deformation continuity and boundary-gradient consistency into the segmentation process, producing more complete and physically consistent landslide masks and improving the Boundary F1 score by 5.52 percentage points over symmetric fusion alone. What are the implications of the main findings? Explicitly embedding InSAR deformation physics improves the reliability and interpretability of high-risk landslide mapping by reducing missed landslides and helping suppress false alarms caused by non-landslide deformation. The INT8 lightweight student model achieves 79.24 percent mIoU at 218 FPS, with a latency of 4.6 ms and GPU memory consumption of only 0.54 GB, supporting efficient operational surveys over large mountainous areas. High-risk landslide extraction from remote sensing imagery is a fundamental task for geological disaster prevention, emergency response, and land-use planning in mountainous regions. Although deep-learning semantic segmentation has substantially advanced landslide detection from optical imagery, existing methods still suffer from three critical limitations: (i) Interferometric Synthetic Aperture Radar (InSAR) deformation data are treated as auxiliary channels and dominated by optical features during fusion; (ii) predicted masks exhibit fragmented boundaries and incomplete delineation due to the absence of deformation continuity constraints reflecting the physical coherence of slope movements; and (iii) heavy Transformer backbones hinder practical deployment over large areas. To address these issues, we propose a Deformation-Integrity-Guided Symmetric Fusion Network (DIGSFNet) for high-risk landslide extraction from InSAR and optical imagery. The framework consists of three components: a Symmetric Deformation-Aware Encoder (SDAE) that treats InSAR and optical modalities as equal information sources through modality-aware adapters and dynamic sparse cross-modal fusion; a Deformation Integrity Prior Decoder (DIPD) that imposes deformation continuity and boundary-gradient consistency as physical priors to enforce mask completeness and boundary accuracy; and a Lightweight Deployable Student Network (LDSN) obtained via cross-modal knowledge distillation and INT8 quantization for efficient inference. Experiments on the Nanning High-Hazard Landslide Segmentation (Nanning-HHLS) dataset and the public HAEFNet benchmark covering the Qinghai–Tibet–Sichuan landslide-prone regions show that the full DIGSFNet achieves state-of-the-art extraction accuracy, reaching 83.57% and 78.92% mIoU on the two datasets and surpassing the strongest competing method by 2.63 and 3.68 percentage points with a Recall of 91.48% on Nanning-HHLS, while its distilled lightweight student retains 79.24% mIoU at 218 frames per second after INT8 quantization, enabling efficient large-area operational deployment. [ABSTRACT FROM AUTHOR]
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  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/rs18162692
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        Text: English
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    Subjects:
      – SubjectFull: Radar interferometry
        Type: general
      – SubjectFull: Landslide hazard analysis
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      – SubjectFull: Image fusion
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      – SubjectFull: Remote-sensing images
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      – SubjectFull: Deep learning
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      – SubjectFull: Nanning (China)
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      – TitleFull: DIGSFNet: Deformation-Integrity-Guided Symmetric Fusion Network for High-Risk Landslide Extraction from Multi-Source Remote Sensing Images.
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              Text: Aug2026
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