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 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 lick em sticks candy 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 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 digiweigh digital pocket scale SAR stack is required to be processed.