MMPhU-Net: A Novel Multi-Model Fusion Phase Unwrapping Network for Large-Gradient Subsidence Deformation

Abstract

The problem of phase unwrapping (PhU) in the large-gradient deformation areas is the bottleneck problem of interferometric synthetic aperture radar (InSAR) data processing. However, the extraction of large-gradient deformation areas is one of the key issues in coal mining deformation monitoring. Here, we propose a novel multi-model fusion PhU Network, abbreviated as MMPhU-Net, and apply it to the extraction of large-gradient deformation areas. The major advantages of MMPhU-Net are: 1) MMPhU-Net combines the advantages of different basic network models, which can improve the model convergence speed and phase gradient estimation accuracy. MMPhU-Net can improve the lack of recognition effect of a single basic model; 2) Different from existing deep learning PhU methods, MMPhU-Net directly estimates the gradient ambiguity numbers, k, so its phase gradient estimation completely breaks through the (-π, π) limitation. Therefore, MMPhU-Net can obtain ideal PhU results in large-gradient deformation areas. In addition, optimization algorithm models are used to optimize the estimation results of the multi-model fusion network. Subsequently, the obtained k and a novel two-step filtering method are combined to obtain the final PhU results. Through the verifications of simulated data sets and realistic GaoFen-3 SAR data sets, the proposed MMPhU-Net method can achieve superior excellent results than the commonly used PhU method.

Near Real-Time Monitoring of Large Gradient Nonlinear Subsidence in Mining Areas: A Hybrid SBAS-InSAR Method Integrating Robust Sequential Adjustment and Deep Learning

“…In mining scenarios, significant nonlinear subsidence with large gradients often arises as the mining face advances. Conventional phase unwrapping methods, such as branch-cut and minimum cost flow, tend to underestimate the subsidence with large gradients, leading to biases in the subsequent subsidence parameter estimates [6]. Therefore, it is essential to employ unwrapping methods suitable for large gradient subsidence.…”