时序InSAR同质样本选取算法研究

Synthetic Aperture Radar Interferometry (InSAR) technique for distributed scatterers has become one of the current flavors. Inaccurate estimation of the covariance matrix is regarded as the most important source of error in such applications. The previous studies, named statistically homogeneous pixel selection algorithms, have demonstrated their values to refine the estimate accuracy for each target without loss of image resolution. In this paper, we review these methods globally under parametric and non-parametric statistical framework. The updated one will then be presented by integrating the advantages of the parametric statistics, and evaluated through synthetic and real data. Based on these algorithms, we finally introduce our SHPS-InSAR open-source toolbox, designed for homogeneous pixel selection and covariance matrix estimation. The toolbox includes almost all methods presented in this paper and tries to provide the optimal observable for the researchers in the field.