An InSAR Phase Unwrapping Framework for Large-scale and Complex Events
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917356783009792 |
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| author | Song, Yijia Biggs, Juliet Achim, Alin Popescu, Robert Orrego, Simon Anantrasirichai, Nantheera |
| author_facet | Song, Yijia Biggs, Juliet Achim, Alin Popescu, Robert Orrego, Simon Anantrasirichai, Nantheera |
| contents | Phase unwrapping remains a critical and challenging problem in InSAR processing, particularly in scenarios involving complex deformation patterns. In earthquake-related deformation, shallow sources can generate surface-breaking faults and abrupt displacement discontinuities, which severely disrupt phase continuity and often cause conventional unwrapping algorithms to fail. Another limitation of existing learning-based unwrapping methods is their reliance on fixed and relatively small input sizes, while real InSAR interferograms are typically large-scale and spatially heterogeneous. This mismatch restricts the applicability of many neural network approaches to real-world data. In this work, we present a phase unwrapping framework based on a diffusion model, developed to process large-scale interferograms and to address phase discontinuities caused by deformation. By leveraging a diffusion model architecture, the proposed method can recover physically consistent unwrapped phase fields even in the presence of fault-related phase jumps. Experimental results on both synthetic and real datasets demonstrate that the method effectively addresses discontinuities associated with near-surface deformation and scales well to large InSAR images, offering a practical alternative to manual unwrapping in challenging scenarios. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_21378 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An InSAR Phase Unwrapping Framework for Large-scale and Complex Events Song, Yijia Biggs, Juliet Achim, Alin Popescu, Robert Orrego, Simon Anantrasirichai, Nantheera Computer Vision and Pattern Recognition Artificial Intelligence Geophysics Phase unwrapping remains a critical and challenging problem in InSAR processing, particularly in scenarios involving complex deformation patterns. In earthquake-related deformation, shallow sources can generate surface-breaking faults and abrupt displacement discontinuities, which severely disrupt phase continuity and often cause conventional unwrapping algorithms to fail. Another limitation of existing learning-based unwrapping methods is their reliance on fixed and relatively small input sizes, while real InSAR interferograms are typically large-scale and spatially heterogeneous. This mismatch restricts the applicability of many neural network approaches to real-world data. In this work, we present a phase unwrapping framework based on a diffusion model, developed to process large-scale interferograms and to address phase discontinuities caused by deformation. By leveraging a diffusion model architecture, the proposed method can recover physically consistent unwrapped phase fields even in the presence of fault-related phase jumps. Experimental results on both synthetic and real datasets demonstrate that the method effectively addresses discontinuities associated with near-surface deformation and scales well to large InSAR images, offering a practical alternative to manual unwrapping in challenging scenarios. |
| title | An InSAR Phase Unwrapping Framework for Large-scale and Complex Events |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Geophysics |
| url | https://arxiv.org/abs/2603.21378 |