Feature Pyramid and Global Attention Network Approach to Insar Two-Dimensional Phase Unwrapping

Chen, Xiaomao; Wu, Qi; He, Chao

doi:10.1109/igarss46834.2022.9884280

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…The feature maps obtained by adding attention and the global average pooling branch are added to form the ultimate output, further improving the performance of the FPA module. The GAU [19] module introduces a mechanism to assign weights to another feature based on the input feature, enabling the network to focus on crucial information. The network structure of the GAU module, depicted in Figure 2, involves several key steps.…”

Section: Proposed Network Model Structuresmentioning

confidence: 99%

“…The residual connections of ResNet help alleviate the gradient vanishing issue, speeding up the training process and enabling the network to learn complex features more effectively with increased depth. Moreover, the incorporation of GAU and FPA helps mitigate the feature information loss in module connections [19][20][21][22]. GAU guides the network to focus on global information, while FPA assists the network with capturing features at different scales, enhancing the robustness of phase unwrapping.…”

Section: Introductionmentioning

confidence: 99%

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Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet

Chen,

Zhang,

Qin

et al. 2024

Remote Sensing

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Two-dimensional phase unwrapping (2-D PU) is vital for reconstructing Earth’s surface topography and displacement from interferometric synthetic aperture radar (InSAR) data. Conventional algorithms rely on the postulate, but this assumption is often insufficient due to abrupt topographic changes and severe noise. To address this challenge, our research proposes a novel approach utilizing deep convolutional neural networks inspired by the U-Net architecture to estimate phase gradient information. Our approach involves downsampling the input data to extract crucial features, followed by upsampling to restore spatial resolution. We incorporate two attention mechanisms—feature pyramid attention (FPA) and global attention upsample (GAU)—and a residual structure in the network’s structure. Thus, we construct ResDANet (residual and dual attention net). We rigorously train ResDANet utilizing simulated datasets and employ an L1-norm objective function to minimize the disparity between unwrapped phase gradients and those calculated by ResDANet, yielding the final 2-D PU results. The network is rigorously trained using two distinct training strategies and encompassing three types of simulated datasets. ResDANet exhibits excellent robust performance and efficiency on simulated data and real data, such as China’s Three Gorges and an Italian volcano.

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Section: Proposed Network Model Structuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet

Chen,

Zhang,

Qin

et al. 2024

Remote Sensing

Self Cite

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“…The network has fewer layers, high accuracy, and fast execution speed. Chen Xiaomao proposed a lightweight network that combines two attention mechanisms [20]. This method is characterized by its time efficiency.…”

Section: Introductionmentioning

confidence: 99%

An Anti-Noise-Designed Residual Phase Unwrapping Neural Network for Digital Speckle Pattern Interferometry

Wang,

Cao,

Lan

et al. 2024

Optics

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DSPI (Digital Speckle Pattern Interferometry) is a non-destructive optical measurement technique that obtains phase information of an object through phase unwrapping. Traditional phase unwrapping algorithms depend on the quality of the images, which demands preprocessing such as filtering and denoising. Moreover, the unwrapping time is highly influenced by the size of the images. In this study, we proposed a new deep learning-based phase unwrapping algorithm combining the residual network and U-Net network. Additionally, we incorporated an improved SSIM function as the loss function based on camera characteristics. The experimental results demonstrated that the proposed method achieved higher quality in highly noisy phase unwrapping maps compared to traditional algorithms, with SSIM values consistently above 0.98. In addition, we applied image stitching to the network to process maps of various sizes and the unwrapping time remained around 1 s even for larger images. In conclusion, our proposed network is able to achieve efficient and accurate phase unwrapping.

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Feature Pyramid and Global Attention Network Approach to Insar Two-Dimensional Phase Unwrapping

Cited by 2 publications

References 14 publications

Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet

Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet

An Anti-Noise-Designed Residual Phase Unwrapping Neural Network for Digital Speckle Pattern Interferometry

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