Adaptive Shortest-Path Network Optimization for Phase Unwrapping in GB-InSAR.
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| Title: | Adaptive Shortest-Path Network Optimization for Phase Unwrapping in GB-InSAR. |
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| Authors: | Bai, Zechao1 (AUTHOR), Wang, Jiqing1,2 (AUTHOR), Wang, Yanping1 (AUTHOR) wangyp@ncut.edu.cn, Yu, Kuai2 (AUTHOR), Shi, Haitao2 (AUTHOR), Shen, Wenjie1 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 7, p1090. 19p. |
| Subjects: | Phase unwrapping (Digital image processing), Radar interferometry, Coherence (Physics), Measurement errors |
| Abstract: | Highlights: What are the main findings? An Adaptive Shortest-Path Network (ASPN) method is proposed for GB-InSAR phase unwrapping in long-term monitoring. ASPN outperforms the Delaunay network in both controlled and stope slope experiments, and also shows better performance than APSP. What are the implications of the main findings? The method effectively reduces unwrapping error propagation in complex deformation areas. It provides a more robust solution for long-term and dynamically updated GB-InSAR deformation monitoring. Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) is widely used for geohazard and infrastructure health assessment because it enables high-precision deformation monitoring. However, long-term time series observations often contain phase discontinuities caused by localized deformation with large spatial gradients, which can severely compromise phase unwrapping reliability. To address this limitation, we propose an Adaptive Shortest-Path Network (ASPN) method for GB-InSAR phase unwrapping. A temporal sliding window strategy is used to partition the acquisition stream into processing units. Within each unit, arc quality is quantified by least squares inversion using the mean square error (MSE) and temporal coherence. The unreliable arcs are removed, and the network is then reconnected using Dijkstra's shortest-path algorithm to improve unwrapping stability and accuracy. The method is evaluated on a corner reflector-controlled deformation dataset and a stope slope dataset. In the controlled experiment, ASPN reduces the root mean square error (RMSE) of cumulative deformation from 1.684 mm to 0.037 mm, representing a 97.8% reduction, while in the stope slope experiment, it reduces the mean phase residual by 30.3% relative to the Delaunay network and by 11.6% relative to APSP. Overall, ASPN provides an efficient dynamic update mechanism and a robust, high-accuracy solution for long-term GB-InSAR time series deformation monitoring. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? An Adaptive Shortest-Path Network (ASPN) method is proposed for GB-InSAR phase unwrapping in long-term monitoring. ASPN outperforms the Delaunay network in both controlled and stope slope experiments, and also shows better performance than APSP. What are the implications of the main findings? The method effectively reduces unwrapping error propagation in complex deformation areas. It provides a more robust solution for long-term and dynamically updated GB-InSAR deformation monitoring. Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) is widely used for geohazard and infrastructure health assessment because it enables high-precision deformation monitoring. However, long-term time series observations often contain phase discontinuities caused by localized deformation with large spatial gradients, which can severely compromise phase unwrapping reliability. To address this limitation, we propose an Adaptive Shortest-Path Network (ASPN) method for GB-InSAR phase unwrapping. A temporal sliding window strategy is used to partition the acquisition stream into processing units. Within each unit, arc quality is quantified by least squares inversion using the mean square error (MSE) and temporal coherence. The unreliable arcs are removed, and the network is then reconnected using Dijkstra's shortest-path algorithm to improve unwrapping stability and accuracy. The method is evaluated on a corner reflector-controlled deformation dataset and a stope slope dataset. In the controlled experiment, ASPN reduces the root mean square error (RMSE) of cumulative deformation from 1.684 mm to 0.037 mm, representing a 97.8% reduction, while in the stope slope experiment, it reduces the mean phase residual by 30.3% relative to the Delaunay network and by 11.6% relative to APSP. Overall, ASPN provides an efficient dynamic update mechanism and a robust, high-accuracy solution for long-term GB-InSAR time series deformation monitoring. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18071090 |
