Dual-Features Student-t Distribution Mixture Model Based Remote Sensing Image Registration

In the work, we present a novel multiviewpoint and multitemporal remote sensing image registration method based on a dual-features Student-t distribution mixture model (DSMM) under a variational Bayesian (VB) framework. The main contributions of the work are: 1) guided image filter (GIF) is adopted to smooth edges and strengthen ridges of images for heightening characteristics of feature point; 2) a Student-t distribution mixture model (SMM) based DSMM designs a global and local descriptor to estimate correspondences from local to global scale; and 3) local structure constraints are designed to preserve relationships of neighbors of points and the scale of neighborhood structure of points to constrain transformation. The experimental results demonstrate the better performance of our DSMM against five state-of-the-art methods.

Automated summary

This work presents a novel method for remote sensing image registration based on a dual-features Student-t distribution mixture model, which utilizes guided image filter to enhance characteristics of feature points. Experimental results demonstrate the superior performance of this method compared to five state-of-the-art methods.