Abstract
Phase unwrapping is a critical step in synthetic aperture radar interferometry (InSAR) data processing chains. In almost all phase unwrapping methods, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step. The phase continuity assumption is not always satisfied due to the presence of noise and abrupt terrain changes; therefore, it is difficult to get the correct phase gradient. In this paper, we propose a robust least squares phase unwrapping method that works via a phase gradient estimation network based on the encoder–decoder architecture (PGENet) for InSAR. In this method, from a large number of wrapped phase images with topography features and different levels of noise, the deep convolutional neural network can learn global phase features and the phase gradient between adjacent pixels, so a more accurate and robust phase gradient can be predicted than that obtained by PGE-PCA. To get the phase unwrapping result, we use the traditional least squares solver to minimize the difference between the gradient obtained by PGENet and the gradient of the unwrapped phase. Experiments on simulated and real InSAR data demonstrated that the proposed method outperforms the other five well-established phase unwrapping methods and is robust to noise.
Highlights
Synthetic aperture radar interferometry (InSAR) is playing an increasingly important role in the field of surface deformation monitoring and topographic mapping [1,2,3].The InSAR system uses two co-registered complex images from different viewing angles to obtain the two-dimensional interferometric phase images
Recent studies [28,29,30,31,32] have indicated that the encoder–decoder architecture based on deep convolutional neural networks (DCNN) can learn the global features from a large number of input images with different levels of noise or other disturbances, which is useful for obtaining the robust phase gradient from noisy wrapped phase images
From lots of wrapped phase images with topography features and different levels of noise, PGENet can extract global high-level phase features and recognize the phase gradient between adjacent pixels, so the more accurate and robust phase gradient can be estimated by PGENet than that obtained by PGE-PCA
Summary
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