A Fast 2-D Phase Unwrapping Algorithm Based on Convolutional Neural Network
Two-dimensional phase unwrapping (2-D PU) is the process of converting the measured phase into the real phase in interferometric signal processing. Reliable unwrapping results are critical for digital elevation model generation using interferometric synthetic aperture radar (InSAR) and interferometr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10198351/ |
| _version_ | 1797738976041238528 |
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| author | Han Li Heping Zhong Zhen Tian Peng Zhang Jinsong Tang |
| author_facet | Han Li Heping Zhong Zhen Tian Peng Zhang Jinsong Tang |
| author_sort | Han Li |
| collection | DOAJ |
| description | Two-dimensional phase unwrapping (2-D PU) is the process of converting the measured phase into the real phase in interferometric signal processing. Reliable unwrapping results are critical for digital elevation model generation using interferometric synthetic aperture radar (InSAR) and interferometric synthetic aperture sonar (InSAS). The majority of previous research has concentrated on accuracy, whereas the computational efficiency must be taken into account for the interferometric measurement system that requires real-time processing. This article proposes a low-time-consuming algorithm that can accomplish high-precision 2-D PU for this application scenario. The neural network and a new path-based 2-D PU algorithm make up this algorithm. First, the incorrect region in the gradient field is predicted and corrected using the neural network. The output channelwise variance is then calculated and used to generate the quality maps. Finally, to achieve phase reconstruction, the path-based algorithm performs path planning and flooding integral according to quality maps and compensated gradient. This article also provides a recommended data structure implementation to ensure the algorithm's high efficiency. Experimental results using InSAR and InSAS data show that the proposed algorithm is highly efficient and accurate. |
| first_indexed | 2024-03-12T13:51:35Z |
| format | Article |
| id | doaj.art-0dfa02393412485a864b8e03ebc573ee |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-12T13:51:35Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-0dfa02393412485a864b8e03ebc573ee2023-08-22T23:00:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01167518752810.1109/JSTARS.2023.329898910198351A Fast 2-D Phase Unwrapping Algorithm Based on Convolutional Neural NetworkHan Li0https://orcid.org/0000-0003-3779-6631Heping Zhong1https://orcid.org/0000-0002-2798-1430Zhen Tian2https://orcid.org/0000-0002-6970-7958Peng Zhang3https://orcid.org/0000-0002-5710-165XJinsong Tang4https://orcid.org/0000-0002-0805-3786Naval Institute of Underwater Acoustic Technology, Naval University of Engineering, Wuhan, ChinaNaval Institute of Underwater Acoustic Technology, Naval University of Engineering, Wuhan, ChinaNaval Institute of Underwater Acoustic Technology, Naval University of Engineering, Wuhan, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha, ChinaNaval Institute of Underwater Acoustic Technology, Naval University of Engineering, Wuhan, ChinaTwo-dimensional phase unwrapping (2-D PU) is the process of converting the measured phase into the real phase in interferometric signal processing. Reliable unwrapping results are critical for digital elevation model generation using interferometric synthetic aperture radar (InSAR) and interferometric synthetic aperture sonar (InSAS). The majority of previous research has concentrated on accuracy, whereas the computational efficiency must be taken into account for the interferometric measurement system that requires real-time processing. This article proposes a low-time-consuming algorithm that can accomplish high-precision 2-D PU for this application scenario. The neural network and a new path-based 2-D PU algorithm make up this algorithm. First, the incorrect region in the gradient field is predicted and corrected using the neural network. The output channelwise variance is then calculated and used to generate the quality maps. Finally, to achieve phase reconstruction, the path-based algorithm performs path planning and flooding integral according to quality maps and compensated gradient. This article also provides a recommended data structure implementation to ensure the algorithm's high efficiency. Experimental results using InSAR and InSAS data show that the proposed algorithm is highly efficient and accurate.https://ieeexplore.ieee.org/document/10198351/Convolutional neural networkdigital elevation model (DEM)interferometric synthetic aperture radar (InSAR)interferometric synthetic aperture sonar (InSAS)phase unwrappingreal-time processing |
| spellingShingle | Han Li Heping Zhong Zhen Tian Peng Zhang Jinsong Tang A Fast 2-D Phase Unwrapping Algorithm Based on Convolutional Neural Network IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Convolutional neural network digital elevation model (DEM) interferometric synthetic aperture radar (InSAR) interferometric synthetic aperture sonar (InSAS) phase unwrapping real-time processing |
| title | A Fast 2-D Phase Unwrapping Algorithm Based on Convolutional Neural Network |
| title_full | A Fast 2-D Phase Unwrapping Algorithm Based on Convolutional Neural Network |
| title_fullStr | A Fast 2-D Phase Unwrapping Algorithm Based on Convolutional Neural Network |
| title_full_unstemmed | A Fast 2-D Phase Unwrapping Algorithm Based on Convolutional Neural Network |
| title_short | A Fast 2-D Phase Unwrapping Algorithm Based on Convolutional Neural Network |
| title_sort | fast 2 d phase unwrapping algorithm based on convolutional neural network |
| topic | Convolutional neural network digital elevation model (DEM) interferometric synthetic aperture radar (InSAR) interferometric synthetic aperture sonar (InSAS) phase unwrapping real-time processing |
| url | https://ieeexplore.ieee.org/document/10198351/ |
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