Local residue coupling strategies by neural network for InSAR phase unwrapping

1997 ◽  
Author(s):  
Alberto Refice ◽  
Giuseppe Satalino ◽  
Maria T. Chiaradia
Keyword(s):  
Neural Network ◽  
Phase Unwrapping ◽  
Local Residue ◽  
Coupling Strategies

Phase unwrapping by a random artificial neural network

1997 ◽  
Author(s):  
Zhengdong Wang ◽  
Dapeng Yan ◽  
Feng Liu ◽  
Anzhi He
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Phase Unwrapping ◽  
Artificial Neural

scholarly journals Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3691
Author(s):  
Jian Liang ◽  
Junchao Zhang ◽  
Jianbo Shao ◽  
Bofan Song ◽  
Baoli Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
3D Imaging ◽  
Phase Unwrapping ◽  
Training Data ◽  
Fringe Projection ◽  
Second Step ◽  
Network Configuration ◽  
Simulation Data ◽  
The Neural Network ◽  
Key Features

Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.

Direct and accurate phase unwrapping with deep neural network

2020 ◽  
Vol 59 (24) ◽  
pp. 7258
Author(s):  
Yi Qin ◽  
Shujia Wan ◽  
Yuhong Wan ◽  
Jiawen Weng ◽  
Wei Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Phase Unwrapping

Phase unwrapping method for point diffraction interferometer based on residual auto encoder neural network

2021 ◽  
Vol 138 ◽  
pp. 106405
Author(s):  
Zhuo Zhao ◽  
Bing Li ◽  
Xiaoqin Kang ◽  
Jiasheng Lu ◽  
Tongkun Liu
Keyword(s):  
Neural Network ◽  
Phase Unwrapping

InSAR Phase Unwrapping using Convolutional Neural Network

2020 ◽  
Author(s):  
Francesco Calvanese ◽  
Francescopaolo Sica ◽  
Giuseppe Scarpa ◽  
Paola Rizzoli
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Phase Unwrapping

scholarly journals A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiyong Wang ◽  
Lu Li ◽  
Yaran Yu ◽  
Jian Wang ◽  
Zhenjin Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Land Subsidence ◽  
Mining Area ◽  
Phase Unwrapping ◽  
New Method ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Coal Mining Area ◽  
Low Coherence

Large-scale and high-intensity mining underground coal has resulted in serious land subsidence. It has caused a lot of ecological environment problems and has a serious impact on the sustainable development of economy. Land subsidence cannot be accurately monitored by InSAR (interferometric synthetic aperture radar) due to the low coherence in the mining area, excessive deformation gradient, and the atmospheric effect. In order to solve this problem, a novel phase unwrapping method based on U-Net convolutional neural network was constructed. Firstly, the U-Net convolutional neural network is used to extract edge to automatically obtain the boundary information of the interferometric fringes in the region of subsidence basin. Secondly, an edge-linking algorithm is constructed based on edge growth and predictive search. The interrupted interferometric fringes are connected automatically. The whole and continuous edges of interferometric fringes are obtained. Finally, the correct phase unwrapping results are obtained according to the principle of phase unwrapping and the wrap-count (integer jump of 2π) at each pixel by edge detection. The Huaibei Coalfield in China was taken as the study area. The real interferograms from D-InSAR (differential interferometric synthetic aperture radar) processing used Sentinel-1A data which were used to verify the performance of the new method. Subsidence basins with clear interferometric fringes, interrupted interferometric fringes, and confused interferometric fringes are selected for experiments. The results were compared with the other methods, such as MCF (minimum cost flow) method. The tests showed that the new method based on U-Net convolutional neural network can resolve the problem that is difficult to obtain the correct unwrapping phase due to interrupted or partially confused interferometric fringes caused by low coherence or other reasons in the coal mining area. Hence, the new method can help to accurately monitor the subsidence in mining areas under different conditions using InSAR technology.

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