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

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.