InSAR Phase Unwrapping Algorithm Based on Deep GAN

• Published in: Journal of function spaces Vol. 2023; pp. 1 - 11
• Main Authors: Wang, Chenxia, Sun, Pingli, Li, Zheng, Tang, Linlin
• Format: Journal Article
• Language: English
• Published: New York Hindawi 27.03.2023; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Algorithms; Brain research; Classification; Deep learning; Efficiency; Feature extraction; Finite element method; Interferometry; Learning theory; Liquors; Machine learning; Neural networks; Phase error; Phase unwrapping; Remote sensing; Signal to noise ratio; Synthetic aperture radar
• ISSN: 2314-8896; 2314-8888
• DOI: 10.1155/2023/9230780

At present, the traditional phase unwrapping algorithm is difficult to balance the accuracy and unwrapping efficiency. The traditional phase unwrapping algorithm is difficult to balance the accuracy and efficiency in the phase unwrapping experiments of simulated and measured topographic interferograms. In this paper, the phase unwrapping technology will be studied under the framework of deep learning theory according to the development trend of InSAR intelligence. A phase unwrapping algorithm based on deep GAN is proposed. The model structure includes dense convolution layer blocks and series structure, so that the network can achieve a better balance between feature extraction and detail preservation of interferogram. This is helpful to improve the phase unwrapping accuracy and training efficiency of the network. The experimental results show that the algorithm has a good expansion effect on the interferogram with high signal-to-noise ratio. The synthesis algorithm makes full use of the advantages of the branch cutting method and the finite element method. The phase reliable region and unreliable region are determined, and the transmission of phase error from the unreliable region to reliable region is effectively avoided. The accuracy of the phase unwrapping results in the reliable region is ensured, and the overall phase unwrapping convergence accuracy is greatly improved.