A Deep Convolutional Neural Network With Multiscale Feature Dynamic Fusion for InSAR Phase Filtering

Interferometric phase filtering is a crucial step in the interferometric synthetic aperture radar (InSAR) data processing, which is also important for improving the accuracy of topography mapping and deformation monitoring. Most of the commonly used phase filtering methods perform windowing computations based on the statistical characteristics of a single interferogram in the spatial or frequency domain. However, the difficulty in taking into account the diversity and complexity of the phase image results in filtering methods with weak denoising, limited detail preservation, and poor generalization ability. At the same time, regardless of the spatial or frequency domain, improved phase filtering performance inevitably leads to the problem of declining effectiveness. This paper proposes a phase filtering method (MSFF-DCNN) based on the deep convolution neural network (DCNN) with Multi-scale feature dynamic fusion. Unlike the traditional feedforward neural networks (FNN), the proposed method adopts a strategy of multi-scale feature dynamic fusion that accounts for the deep and shallow features of the interferometric phase while also taking into account image detail preservation and noise suppression during phase filtering. Based on both subjective and objective evaluations, the experimental results using the simulated data prove that the proposed method has better noise suppression and detail preservation than the commonly used methods and that the filtering performance is less dependent on noise level. Experiments using the real data confirm that the proposed method has better generalization ability and can meet the precision requirements of practical applications. The method presented in this paper can provide a new approach for research in high-precision InSAR data processing technology while also offering technical support for practical InSAR applications.