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In the spatial target surveillance and astronomical observation applications, image matching processing is the key procedure 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 stars or 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 feature point extraction quite difficult. In this paper, a new type of image matching method based on the description of Multi-scale Geometric Invariant Features (MGIF) is proposed, which uses the Rolling Guidance Filter (RGF) to perform preprocessing for the input images. By virtue of the excellent edge-preserving performance of the Joint Bilateral Filter in RGF, the integrities of contour profile of the star points are guaranteed effectively while the interference and other noise in the background are suppressed. Then the segmented and morphology methods are applied to extract star points and get the centroid of star points to form the feature point constellation. Considering the cross ratio of two lines in projection transformation model of image matching is a geometric invariant, a multi-scale geometric invariants based function, which uses the scaling of RGF as a reference to describe the relative spatial positions of matching points more accurately, is constructed to evaluate the level of similarity between star points according to the relative position of each points in the constellation. Subsequently, Random Sample Consensus（RANSAC）method is adopted to remove the mismatching star points and calculate the rigid transform matrix and other registration parameters. Digital simulation and practical processing results demonstrate that the proposed method can achieve higher matching accuracy and robustness for the starry images with low SNR and complex backgrounds.
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