Deep-Learning-Based Phase Discontinuity Prediction for 2-D Phase Unwrapping of SAR Interferograms
| العنوان: | Deep-Learning-Based Phase Discontinuity Prediction for 2-D Phase Unwrapping of SAR Interferograms |
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| المؤلفون: | Zhipeng Wu, Daqing Ge, Teng Wang, Yingjie Wang, Robert Wang |
| المصدر: | IEEE Transactions on Geoscience and Remote Sensing. 60:1-16 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Azimuth, Discontinuity (linguistics), Noise (signal processing), Aliasing, Computer science, Interferometric synthetic aperture radar, Phase (waves), General Earth and Planetary Sciences, Electrical and Electronic Engineering, Classification of discontinuities, Algorithm, Decorrelation |
| الوصف: | Phase unwrapping is a critical step of interferometric synthetic aperture radar (InSAR) processing, and its accuracy directly determines the reliability of subsequent applications. Many phase unwrapping methods have been proposed, most of which assume that the phase has spatial continuity, while decorrelation noise and aliasing fringes invalidate the assumptions, resulting in poor performance of these methods. To obtain more reliable unwrapping results, in this paper, a deep convolutional neural network, called DENet, is proposed for predicting the probabilities of phase discontinuities in interferograms. The main advantages of DENet are: (1) using branching structure to extract detailed and high-level features separately, to retain details while making full use of contextual information; (2) using multi-channel input, including interferogram, range/azimuthal phase gradients, and residues map, to provide effective guidance for discontinuity prediction; and (3) using a single network to estimate phase discontinuities in both range and azimuth directions simultaneously. To train the network, a data set simulation strategy is proposed to generate enough training samples. The strategy considers a variety of phase components such as terrain-related phase, random deformation, atmospheric turbulence, and noise. The phase discontinuity estimated by DENet is then converted to costs in the minimum cost flow solver of SNAPHU to obtain the final unwrapped phase. Based on validations of simulated and real interferograms, the proposed method exhibits excellent performance compared to traditional and deep learning unwrapping methods. The proposed method can effectively unwrap large-scale, low-quality interferograms, which is expected to significantly improve the accuracy of InSAR applications. |
| تدمد: | 1558-0644 0196-2892 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::c778758199fcb15d4c7ed64bbfe17e91 https://doi.org/10.1109/tgrs.2021.3121906 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........c778758199fcb15d4c7ed64bbfe17e91 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15580644 01962892 |
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