PUMA-SPA: A Phase Unwrapping Method Based on PUMA and Second-Order Polynomial Approximation

Hao, Hongxing; Wu, Lei

doi:10.1109/lgrs.2014.2313854

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…From Equation 1, PU was an illposed inverse problem. As k (x, y) was discrete, the traditional algorithms to solve this kind of problem approximated it by a continuous function to obtain the final solution (e.g., (Hongxing and LingdaPUMA-SPA, 2014)). As mentioned above, the discrete term is usually made continuous by use of the differential term in PU methods based on deep learning.…”

Section: Discussion Of Two Problem Modelsmentioning

confidence: 99%

PUnet: A robust framework for phase unwrapping in interferometric SAR

Liu

Wu²,

Xiaorui³

et al. 2023

Front. Environ. Sci.

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Synthetic Aperture Radar Interferometry (InSAR) has grown significantly over the past few decades, which were mainly used in remote sensing applications. Most InSAR applications (e.g., terrain mapping and monitoring) utilized a key technique called phase unwrapping Phase unwrapping obtained the absolute phase from the wrapped phase for the subsequent application. However, the collected wrapped phase inevitably contains noise due to the influence of factors such as atmosphere and temperature in the InSAR acquisition stage. This noise made it challenging to obtain the absolute phase from the wrapped phase. This study proposed a deep learning framework (PUnet) for phase unwrapping form InSAR data. pUnet was a robust framework using U-net as the basic structure combined with an attention mechanism and positional encoding, facilitating accurate phase unwrapping from the wrapped phase. Through comparative experiments with typical phase unwrapping algorithms, we demonstrated that pUnet could obtain absolute phases with high accuracy and robustness than from the wrapped phase under various levels of noise.

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Section: Discussion Of Two Problem Modelsmentioning

confidence: 99%

PUnet: A robust framework for phase unwrapping in interferometric SAR

Liu

Wu²,

Xiaorui³

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Although it is computationally efficient to solve Poisson's equation using a multigrid solver as described, there is no inherent regularization or smoothing of the solution in this step. A phase unwrapper that can incorporate a smoothness criterion, such as in previous works , could possibly increase the robustness further. The water‐fat classification only failed in 10 cases of smaller and peripheral clusters during this evaluation, and it may be possible to improve the classification step further.…”

Section: Discussionmentioning

confidence: 99%