MMPhU-Net: A Novel Multi-Model Fusion Phase Unwrapping Network for Large-Gradient Subsidence Deformation
| Autor: | Yandong Gao, Jiaqi Yao, Nanshan Zheng, Shijin Li, Hefang Bian, Yu Tian |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 5137-5146 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1939-1404 2151-1535 |
| DOI: | 10.1109/JSTARS.2024.3362389 |
| Popis: | The problem of phase unwrapping (PhU) in the large-gradient deformation areas is the bottleneck problem of interferometric synthetic aperture radar (InSAR) data processing. However, the extraction of large-gradient deformation areas is one of the key issues in coal mining deformation monitoring. Here, we propose a novel multimodel fusion PhU Network, abbreviated as MMPhU-Net, and apply it to the extraction of large-gradient deformation areas. The major advantages of MMPhU-Net are as follows: First, MMPhU-Net combines the advantages of different basic network models, which can improve the model convergence speed and phase gradient estimation accuracy. MMPhU-Net can improve the lack of recognition effect of a single basic model. Second, different from existing deep learning PhU methods, MMPhU-Net directly estimates the gradient ambiguity numbers, k, so its phase gradient estimation completely breaks through the (−π, π) limitation. Therefore, MMPhU-Net can obtain ideal PhU results in large-gradient deformation areas. In addition, optimization algorithm models are used to optimize the estimation results of the multimodel fusion network. Subsequently, the obtained k and a novel two-step filtering method are combined to obtain the final PhU results. Through the verifications of simulated data sets and realistic GaoFen-3 SAR data sets, the proposed MMPhU-Net method can achieve superior excellent results than the commonly used PhU method. |
| Databáze: | Directory of Open Access Journals |
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