Research On InSAR Phase Unwrapping Algorithm Based On Deep Learning

Phase imaging technologies such as interferometric synthetic aperture radar(InSAR),magnetic resonance imaging(MRI),or optical interferometry,are nowadays widespread and with an increasing usage in monitoring natural disasters,medical imaging,3D modeling.However,phase unwrapping is a key step in phase interference technology and one of its most challenging problems.The so-called phase unwrapping is the process of inferring the absolute phase from the relative phase with an integer multiple of 2?.We introduce A phase unwrapping method based on deep learning.(1)In this paper,the research of InSAR phase unwrapping technology is carried out,and the imaging principle of SAR and InSAR is analyzed.Through the description of InSAR model,the error source of InSAR is analyzed and modeled to simulate InSAR data,which provide the foundation for our deep learning studies.(2)This paper studies the principle of phase unwrapping deeply,and on this basis,it makes a comprehensive summary and analysis of the traditional phase unwrapping method.Including path-based Branch-Cut and quality guidance maps,minimum norm least squares,network programming,and Bayesian-based Markov methods.Each method is analyzed by comparing these methods.The advantages and disadvantages and the most suitable scenes for each,which provides a theoretical basis for improving the efficiency and accuracy of unwinding,and provides ideas for our new method.(3)Therefore,in view of the disadvantages of traditional methods and the current environment of remote sensing big data,this paper proposes a method based on deep learning.This paper uses the Pix2 pix network model to solve the problem,and uses the simulated data to train the model.Prove the effectiveness of the method.