A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion
Phase unwrapping (PhU) is one of the key steps in interferometric synthetic aperture radar (InSAR) data processing, and it is a considerable challenge for PhU in regions with high-noise and large-gradient changes. Deep learning phase unwrapping (DLPU) can better solve this problem. However, a single...
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2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10884057/ |
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| author | Chao Yan Tao Li Yandong Gao Shijin Li Xiang Zhang Xuefei Zhang Di Zhang Huiqin Liu |
| author_facet | Chao Yan Tao Li Yandong Gao Shijin Li Xiang Zhang Xuefei Zhang Di Zhang Huiqin Liu |
| author_sort | Chao Yan |
| collection | DOAJ |
| description | Phase unwrapping (PhU) is one of the key steps in interferometric synthetic aperture radar (InSAR) data processing, and it is a considerable challenge for PhU in regions with high-noise and large-gradient changes. Deep learning phase unwrapping (DLPU) can better solve this problem. However, a single DLPU algorithm still finds it difficult to obtain robust PhU results in regions with large-gradient changes. In addition, the performance of the same training model varies greatly for different data. To solve this problem, this paper combines a deep neural network model with the traditional PhU model and proposes a novel two-stage learning-based phase unwrapping (TLPU) algorithm via multimodel fusion. The major advantages of TLPU are as follows: 1) A high-resolution U-Net (HRU-Net) model trained on a dataset constructed according to InSAR interferometric geometry is utilized for the PhU for the first time, which effectively improves the performance of the DLPU. 2) TLPU utilizes the traditional PhU method to optimize the results of DLPU, addressing the issue of weak generalization ability of a single DLPU, while improving accuracy in areas with large-gradient changes. Experimental analysis was carried out using LT-1 data, and the results show that the proposed TLPU algorithm can achieve superior excellent results in large-gradient change regions compared with the commonly used PhU method, with root mean square errors of only 1.63 rad in experiment 1 and 1.96 rad in experiment 2. |
| format | Article |
| id | doaj-art-2a8382bbcb7e46c89c397199841d76fb |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-2a8382bbcb7e46c89c397199841d76fb2025-08-20T03:47:03ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01187468747910.1109/JSTARS.2025.354132210884057A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel FusionChao Yan0https://orcid.org/0009-0004-4209-7880Tao Li1https://orcid.org/0000-0002-7676-7852Yandong Gao2https://orcid.org/0000-0002-6224-5711Shijin Li3https://orcid.org/0000-0001-9394-389XXiang Zhang4Xuefei Zhang5https://orcid.org/0000-0003-3352-3329Di Zhang6Huiqin Liu7Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of the People's Republic of China, Beijing, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources of the People's Republic of China, Beijing, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources of the People's Republic of China, Beijing, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources of the People's Republic of China, Beijing, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaPhase unwrapping (PhU) is one of the key steps in interferometric synthetic aperture radar (InSAR) data processing, and it is a considerable challenge for PhU in regions with high-noise and large-gradient changes. Deep learning phase unwrapping (DLPU) can better solve this problem. However, a single DLPU algorithm still finds it difficult to obtain robust PhU results in regions with large-gradient changes. In addition, the performance of the same training model varies greatly for different data. To solve this problem, this paper combines a deep neural network model with the traditional PhU model and proposes a novel two-stage learning-based phase unwrapping (TLPU) algorithm via multimodel fusion. The major advantages of TLPU are as follows: 1) A high-resolution U-Net (HRU-Net) model trained on a dataset constructed according to InSAR interferometric geometry is utilized for the PhU for the first time, which effectively improves the performance of the DLPU. 2) TLPU utilizes the traditional PhU method to optimize the results of DLPU, addressing the issue of weak generalization ability of a single DLPU, while improving accuracy in areas with large-gradient changes. Experimental analysis was carried out using LT-1 data, and the results show that the proposed TLPU algorithm can achieve superior excellent results in large-gradient change regions compared with the commonly used PhU method, with root mean square errors of only 1.63 rad in experiment 1 and 1.96 rad in experiment 2.https://ieeexplore.ieee.org/document/10884057/Deep learning (DL)interferometric synthetic aperture radar (INSAR)<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$L_{1}$</tex-math> </inline-formula> </named-content>-normphase unwrapping (PhU) |
| spellingShingle | Chao Yan Tao Li Yandong Gao Shijin Li Xiang Zhang Xuefei Zhang Di Zhang Huiqin Liu A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning (DL) interferometric synthetic aperture radar (INSAR) <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$L_{1}$</tex-math> </inline-formula> </named-content>-norm phase unwrapping (PhU) |
| title | A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion |
| title_full | A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion |
| title_fullStr | A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion |
| title_full_unstemmed | A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion |
| title_short | A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion |
| title_sort | novel two stage learning based phase unwrapping algorithm via multimodel fusion |
| topic | Deep learning (DL) interferometric synthetic aperture radar (INSAR) <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$L_{1}$</tex-math> </inline-formula> </named-content>-norm phase unwrapping (PhU) |
| url | https://ieeexplore.ieee.org/document/10884057/ |
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