Time series phase unwrapping algorithm using LP-norm optimization compressive sensing
Time series phase unwrapping is an essential step in the time series interferometric synthetic aperture radar technique for deformation monitoring. However, traditional unwrapping algorithms are prone to unwrapping errors in the case of intense noise and steep deformation gradients, which directly a...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223000043 |
| _version_ | 1811163488251281408 |
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| author | Shijin Li Shubi Zhang Yandong Gao Tao Li Jiazheng Han Qiang Chen Yansuo Zhang Yu Tian |
| author_facet | Shijin Li Shubi Zhang Yandong Gao Tao Li Jiazheng Han Qiang Chen Yansuo Zhang Yu Tian |
| author_sort | Shijin Li |
| collection | DOAJ |
| description | Time series phase unwrapping is an essential step in the time series interferometric synthetic aperture radar technique for deformation monitoring. However, traditional unwrapping algorithms are prone to unwrapping errors in the case of intense noise and steep deformation gradients, which directly affect the accuracy of the deformation information interpretation. To address this problem, a time series phase unwrapping algorithm using LP-norm optimization compressive sensing is proposed in this paper. This algorithm first transforms the minimizing L0-norm describing compressive sensing technique into a nonconvex problem with weaker constraints, that is, minimizing the LP-norm. Then, an optimized compressed sensing model was proposed by combining the constraint criterion of the phase triplet closure in the temporal domain and the solution strategy of the iterative reweighted least squares method. Improved temporal dimensional phase unwrapping and unwrapping error correction algorithms were developed based on this optimized model. Finally, the proposed algorithm was perfected by combining the above improved methods and the pseudo-three-dimensional phase unwrapping framework. Simulated and Sentinel-1A real datasets verify that the proposed algorithm has better noise robustness, unwrapping stability, and efficiency than traditional algorithms, particularly in the case of a steep deformation gradient and intense noise. The temporal coherence estimated by the proposed algorithm for the real dataset is generally greater than 0.99, which is far superior to traditional algorithms. Furthermore, the proposed algorithm facilitates high-precision interpretation of deformation information. The cross-validation results show that the deformation results obtained by traditional algorithms are underestimated by at least 73 mm/a and 68 mm at the maximum subsidence than the DSInSAR. However, the deformation result obtained by the proposed algorithm is highly consistent with that of the DSInSAR. |
| first_indexed | 2024-04-10T15:06:58Z |
| format | Article |
| id | doaj.art-f25adb7a1723452c8f8117f2241f902b |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-10T15:06:58Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-f25adb7a1723452c8f8117f2241f902b2023-02-15T04:27:26ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-03-01117103182Time series phase unwrapping algorithm using LP-norm optimization compressive sensingShijin Li0Shubi Zhang1Yandong Gao2Tao Li3Jiazheng Han4Qiang Chen5Yansuo Zhang6Yu Tian7School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China; Corresponding author.School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources of P.R. China, Beijing 100048, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaYankuang Energy Group Company Limited Jining No.3 Coal Mine, Jining 272069, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaTime series phase unwrapping is an essential step in the time series interferometric synthetic aperture radar technique for deformation monitoring. However, traditional unwrapping algorithms are prone to unwrapping errors in the case of intense noise and steep deformation gradients, which directly affect the accuracy of the deformation information interpretation. To address this problem, a time series phase unwrapping algorithm using LP-norm optimization compressive sensing is proposed in this paper. This algorithm first transforms the minimizing L0-norm describing compressive sensing technique into a nonconvex problem with weaker constraints, that is, minimizing the LP-norm. Then, an optimized compressed sensing model was proposed by combining the constraint criterion of the phase triplet closure in the temporal domain and the solution strategy of the iterative reweighted least squares method. Improved temporal dimensional phase unwrapping and unwrapping error correction algorithms were developed based on this optimized model. Finally, the proposed algorithm was perfected by combining the above improved methods and the pseudo-three-dimensional phase unwrapping framework. Simulated and Sentinel-1A real datasets verify that the proposed algorithm has better noise robustness, unwrapping stability, and efficiency than traditional algorithms, particularly in the case of a steep deformation gradient and intense noise. The temporal coherence estimated by the proposed algorithm for the real dataset is generally greater than 0.99, which is far superior to traditional algorithms. Furthermore, the proposed algorithm facilitates high-precision interpretation of deformation information. The cross-validation results show that the deformation results obtained by traditional algorithms are underestimated by at least 73 mm/a and 68 mm at the maximum subsidence than the DSInSAR. However, the deformation result obtained by the proposed algorithm is highly consistent with that of the DSInSAR.http://www.sciencedirect.com/science/article/pii/S1569843223000043Interferometric synthetic aperture radarTime series phase unwrappingCompressive sensingLP-norm minimization |
| spellingShingle | Shijin Li Shubi Zhang Yandong Gao Tao Li Jiazheng Han Qiang Chen Yansuo Zhang Yu Tian Time series phase unwrapping algorithm using LP-norm optimization compressive sensing International Journal of Applied Earth Observations and Geoinformation Interferometric synthetic aperture radar Time series phase unwrapping Compressive sensing LP-norm minimization |
| title | Time series phase unwrapping algorithm using LP-norm optimization compressive sensing |
| title_full | Time series phase unwrapping algorithm using LP-norm optimization compressive sensing |
| title_fullStr | Time series phase unwrapping algorithm using LP-norm optimization compressive sensing |
| title_full_unstemmed | Time series phase unwrapping algorithm using LP-norm optimization compressive sensing |
| title_short | Time series phase unwrapping algorithm using LP-norm optimization compressive sensing |
| title_sort | time series phase unwrapping algorithm using lp norm optimization compressive sensing |
| topic | Interferometric synthetic aperture radar Time series phase unwrapping Compressive sensing LP-norm minimization |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223000043 |
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