Method of star image identification based on robust perceptual hash feature

Traditional star image identification methods use adjacent stellar angular distances to form identification features. The missed detection of high-magnitude stars due to noise interference affects their identification accuracy. We propose a star image identification method based on perceptual hash features. The multiple Gaussian distribution superposition constructed by stars within a certain range around the center star forms the unique standard identification image of that star, in which the brightness distribution is determined by stellar magnitudes. The perceptual hash feature of the standard identification image is then used as a standard identification feature. The observation feature is extracted from the identification image as-constructed by the star points extracted from the star image. Finally, the observation feature is matched with the standard feature to identify the star image. Simulation results indicate that the proposed method can efficiently identify star images and effectively prevent missed stars from influencing the matching results.