A Novel Model for Interferometric Phase Reconstruction Based on Multi-Stage Conditional GANs
Reconstructing the interferometric phase in decorrelated regions is a significant challenge in interferometric synthetic aperture radar (InSAR) techniques, as decorrelation disrupts the continuity of phase fringes and obscures critical information. This paper presents a novel two-stage generative ad...
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Copernicus Publications
2025-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-G-2025/11/2025/isprs-annals-X-G-2025-11-2025.pdf |
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| author | M. Abdallah M. Abdallah M. Abdallah X. Ding X. Ding S. Wu S. Wu |
| author_facet | M. Abdallah M. Abdallah M. Abdallah X. Ding X. Ding S. Wu S. Wu |
| author_sort | M. Abdallah |
| collection | DOAJ |
| description | Reconstructing the interferometric phase in decorrelated regions is a significant challenge in interferometric synthetic aperture radar (InSAR) techniques, as decorrelation disrupts the continuity of phase fringes and obscures critical information. This paper presents a novel two-stage generative adversarial network (GAN) framework to address this issue. In the first stage, GAN is designed to reconnect fragmented phase fringes. In the second stage, GAN focuses on reconstructing the phase in masked regions guided by the reconnected fringes achieved from the first stage. The proposed model was trained on a simulated topographic phase with the SRTAM product. The proposed model achieves a structural similarity index (SSIM) of 0.9 and a peak signal-to-noise ratio (PSNR) of 30.4. Then, we conducted a quantitative evaluation with a real interferogram from the Greater Bay Area (GBA). The experiment results demonstrated the generalization capabilities of the proposal model, with an average correlation of 0.8 between the predicted and actual phases. The proposed approach can effectively preserve phase continuity, reconstruct masked areas, and mitigate the impact of decorrelation. It shows potential for use in topographic retrieval and ground deformation monitoring in InSAR applications. |
| format | Article |
| id | doaj-art-465fe4cc58d24afe86e187ea2921e957 |
| institution | Kabale University |
| issn | 2194-9042 2194-9050 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-465fe4cc58d24afe86e187ea2921e9572025-08-20T03:28:44ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502025-07-01X-G-2025111710.5194/isprs-annals-X-G-2025-11-2025A Novel Model for Interferometric Phase Reconstruction Based on Multi-Stage Conditional GANsM. Abdallah0M. Abdallah1M. Abdallah2X. Ding3X. Ding4S. Wu5S. Wu6Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaResearch Institution for Land and Space, The Hong Kong Polytechnic University, Hong Kong, ChinaPublic Works Department, Mansoura University, Mansoura, EgyptDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaResearch Institution for Land and Space, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaResearch Institution for Land and Space, The Hong Kong Polytechnic University, Hong Kong, ChinaReconstructing the interferometric phase in decorrelated regions is a significant challenge in interferometric synthetic aperture radar (InSAR) techniques, as decorrelation disrupts the continuity of phase fringes and obscures critical information. This paper presents a novel two-stage generative adversarial network (GAN) framework to address this issue. In the first stage, GAN is designed to reconnect fragmented phase fringes. In the second stage, GAN focuses on reconstructing the phase in masked regions guided by the reconnected fringes achieved from the first stage. The proposed model was trained on a simulated topographic phase with the SRTAM product. The proposed model achieves a structural similarity index (SSIM) of 0.9 and a peak signal-to-noise ratio (PSNR) of 30.4. Then, we conducted a quantitative evaluation with a real interferogram from the Greater Bay Area (GBA). The experiment results demonstrated the generalization capabilities of the proposal model, with an average correlation of 0.8 between the predicted and actual phases. The proposed approach can effectively preserve phase continuity, reconstruct masked areas, and mitigate the impact of decorrelation. It shows potential for use in topographic retrieval and ground deformation monitoring in InSAR applications.https://isprs-annals.copernicus.org/articles/X-G-2025/11/2025/isprs-annals-X-G-2025-11-2025.pdf |
| spellingShingle | M. Abdallah M. Abdallah M. Abdallah X. Ding X. Ding S. Wu S. Wu A Novel Model for Interferometric Phase Reconstruction Based on Multi-Stage Conditional GANs ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | A Novel Model for Interferometric Phase Reconstruction Based on Multi-Stage Conditional GANs |
| title_full | A Novel Model for Interferometric Phase Reconstruction Based on Multi-Stage Conditional GANs |
| title_fullStr | A Novel Model for Interferometric Phase Reconstruction Based on Multi-Stage Conditional GANs |
| title_full_unstemmed | A Novel Model for Interferometric Phase Reconstruction Based on Multi-Stage Conditional GANs |
| title_short | A Novel Model for Interferometric Phase Reconstruction Based on Multi-Stage Conditional GANs |
| title_sort | novel model for interferometric phase reconstruction based on multi stage conditional gans |
| url | https://isprs-annals.copernicus.org/articles/X-G-2025/11/2025/isprs-annals-X-G-2025-11-2025.pdf |
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