SA-Unet: A Phase Unwrapping method based on DCNN

Feng, Liang; Jia, Tong; Zeng, Zhikang

doi:10.1109/cyber55403.2022.9907391

Cited by 2 publications

(2 citation statements)

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“…Liang et al, proposed the SA-UNet, which employs an encoder-decoder structure and a spatial attention module to perform phase unwrapping with a synthetic dataset. Experiments showed higher accuracy and improved robustness than traditional phase unwrapping methods [49]. Anantrasirichai et al, trained the AlexNet for extracting volcanic deformation.…”

Section: Architectures Of Deep Learning Network In Insar Applicationsmentioning

confidence: 99%

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A Deep-Learning-Facilitated, Detection-First Strategy for Operationally Monitoring Localized Deformation with Large-Scale InSAR

Wang

Zhang

2023

Remote Sensing

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SAR interferometry (InSAR) has emerged in the big-data era, particularly benefitting from the acquisition capability and open-data policy of ESA’s Sentinel-1 SAR mission. A large number of Sentinel-1 SAR images have been acquired and archived, allowing for the generation of thousands of interferograms, covering millions of square kilometers. In such a large-scale interferometry scenario, many applications actually aim at monitoring localized deformation sparsely distributed in the interferogram. Thus, it is not effective to apply the time-series InSAR analysis to the whole image and identify the deformed targets from the derived velocity map. Here, we present a strategy facilitated by the deep learning networks to firstly detect the localized deformation and then carry out the time-series analysis on small interferogram patches with deformation signals. Specifically, we report following-up studies of our proposed deep learning networks for masking decorrelation areas, detecting local deformation, and unwrapping high-gradient phases. In the applications of mining-induced subsidence monitoring and slow-moving landslide detection, the presented strategy not only reduces the computation time, but also avoids the influence of large-scale tropospheric delays and unwrapping errors. The presented detection-first strategy introduces deep learning to the time-series InSAR processing chain and makes the mission of operationally monitoring localized deformation feasible and efficient for the large-scale InSAR.

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Section: Architectures Of Deep Learning Network In Insar Applicationsmentioning

confidence: 99%

“…Deep learning methods were first introduced for phase unwrapping as a segmentation problem [49,[75][76][77][78]. This is an apparent and natural migration, because fringe edges, if clearly exhibited, split the wrapped interferogram into many segmentations.…”

Section: Application To Mining-induced Subsidence Detection With Time...mentioning

confidence: 99%

A Deep-Learning-Facilitated, Detection-First Strategy for Operationally Monitoring Localized Deformation with Large-Scale InSAR

Wang

Zhang

2023

Remote Sensing

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A Deep-Learning-Based Algorithm for Landslide Detection over Wide Areas Using InSAR Images Considering Topographic Features

Li,

Feng,

Zhao

et al. 2024

Sensors

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The joint action of human activities and environmental changes contributes to the frequent occurrence of landslide, causing major hazards. Using Interferometric Synthetic Aperture Radar (InSAR) technique enables the detailed detection of surface deformation, facilitating early landslide detection. The growing availability of SAR data and the development of artificial intelligence have spurred the integration of deep learning methods with InSAR for intelligent geological identification. However, existing studies using deep learning methods to detect landslides in InSAR deformation often rely on single InSAR data, which leads to the presence of other types of geological hazards in the identification results and limits the accuracy of landslide identification. Landslides are affected by many factors, especially topographic features. To enhance the accuracy of landslide identification, this study improves the existing geological hazard detection model and proposes a multi-source data fusion network termed MSFD-Net. MSFD-Net employs a pseudo-Siamese network without weight sharing, enabling the extraction of texture features from the wrapped deformation data and topographic features from topographic data, which are then fused in higher-level feature layers. We conducted comparative experiments on different networks and ablation experiments, and the results show that the proposed method achieved the best performance. We applied our method to the middle and upper reaches of the Yellow River in eastern Qinghai Province, China, and obtained deformation rates using Sentinel-1 SAR data from 2018 to 2020 in the region, ultimately identifying 254 landslides. Quantitative evaluations reveal that most detected landslides in the study area occurred at an elevation of 2500–3700 m with slope angles of 10–30°. The proposed landslide detection algorithm holds significant promise for quickly and accurately detecting wide-area landslides, facilitating timely preventive and control measures.

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SA-Unet: A Phase Unwrapping method based on DCNN

Cited by 2 publications

References 0 publications

A Deep-Learning-Facilitated, Detection-First Strategy for Operationally Monitoring Localized Deformation with Large-Scale InSAR

A Deep-Learning-Facilitated, Detection-First Strategy for Operationally Monitoring Localized Deformation with Large-Scale InSAR

A Deep-Learning-Based Algorithm for Landslide Detection over Wide Areas Using InSAR Images Considering Topographic Features

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