Nonrigid Feature Matching for Remote Sensing Images via Probabilistic Inference With Global and Local Regularizations

In this letter, we propose a probabilistic method for the feature matching of remote sensing images which undergo nonrigid transformations. We start by creating a set of putative correspondences based on the feature similarity and then focus on removing outliers from the putative set and estimating the transformation as well. This is formulated as a maximum likelihood estimation of a Bayesian model with latent variables indicating whether matches in the putative set are inliers or outliers. We impose nonparametric global geometrical constraints on the correspondence using Tikhonov regularizers in a reproducing kernel Hilbert space. We also introduce a local geometrical constraint to preserve local structures among neighboring feature points. The problem is solved by using the expectation-maximization algorithm, and the closed-form solution of the transformation is derived in the maximization step. Moreover, a fast implementation based on sparse approximation is given which reduces the method computation complexity to linearithmic without performance sacrifice. Extensive experiments on real remote sensing images demonstrate accurate results of the proposed method which outperforms current state-of-the-art methods, particularly in case of severe outliers.