Interferometric Phase Reconstruction Based on Probability Generative Model: Toward Efficient Analysis of High-Dimensional SAR Stacks

Interferometric Phase Reconstruction Based on Probability Generative Model: Toward Efficient Analysis of High-Dimensional SAR Stacks

Blog Article

In order to minimize the influence of decorrelation noise on multi-temporal interferometric synthetic aperture radar (MT-InSAR) applications, a series of phase reconstruction methods have been proposed in recent years.Unfortunately, current phase reconstruction methods generally exhibit Heater Hose a low computational efficiency due to their high non-linearity, in particular in the case that the dimension of a SAR stack is high.In this paper, a new approach is proposed to efficiently resolve phase reconstruction problems.This approach is inspired by the theory of probabilistic principle component analysis.A complex valued probability generative model is constructed to portray a phase reconstruction process.

Moreover, in order to resolve such a model, a targeted algorithm based on the idea of expectation maximization is Steering designed and implemented.For validation purposes, the proposed approach is compared to the traditional eigenvalue decomposition-based method by using simulated data and 101 real Sentinel-1A SAR images.The experimental results demonstrate that the proposed method can accelerate the phase reconstruction process drastically, in particular when a high-dimensional SAR stack is required to be processed.