Starry Image Matching Method based on the Description of Multiscale Geometric Invariant Features

摘要

In the spatial target surveillance and astronomical observation applications, image matching processing is the keyprocedure for the multi-temporal starry images or the multi-channel starry images acquired by different imaging sensors.However, the starry images obtained often have low Signal-to-Noise Ratios (SNR), the light intensities of the target starsor spacecrafts in them are vulnerable to background interferences, such as the atmospheric turbulence and the night clouds,etc., and become dim and instable. With the weak texture information of the target stars, all the influences make the featurepoint extraction quite difficult. In this paper, a new type of image matching method based on the description of Multi-scaleGeometric Invariant Features (MGIF) is proposed, which uses the Rolling Guidance Filter (RGF) to perform preprocessingfor the input images. By virtue of the excellent edge-preserving performance of the Joint Bilateral Filter in RGF, theintegrities of contour profile of the star points are guaranteed effectively while the interference and other noise in thebackground are suppressed. Then the segmented and morphology methods are applied to extract star points and get thecentroid of star points to form the feature point constellation. Considering the cross ratio of two lines in projectiontransformation model of image matching is a geometric invariant, a multi-scale geometric invariants based function, whichuses the scaling of RGF as a reference to describe the relative spatial positions of matching points more accurately, isconstructed to evaluate the level of similarity between star points according to the relative position of each points in theconstellation. Subsequently, Random Sample Consensus（RANSAC）method is adopted to remove the mismatching starpoints and calculate the rigid transform matrix and other registration parameters. Digital simulation and practicalprocessing results demonstrate that the proposed method can achieve higher matching accuracy and robustness for thestarry images with low SNR and complex backgrounds.