Abstract: Two problems in the study of star point clustering in high resolution astronomical images: ① The resolution of the astronomical image is higher, and the image processing speed is slower. ② Which clustering algorithm is selected to cluster the star points in the astronomical image is better. In the research, problem 1 uses image segmentation method to improve image processing speed. problem 2 proposes an improved K-means clustering algorithm to solve the traditional K-means clustering algorithm clustering results are susceptible to k-value and The initial clustering center randomly selects the problem of impact. Firstly, the K-means clustering algorithm is used to determine the appropriate k-value based on the preliminary clustering of data. Secondly, the clustering is used to determine the initial clustering center by data clustering. Finally, K-means clustering is used. The algorithm performs clustering. The simulation results of MATLAB show that the clustering results and efficiency of the algorithm are better than other clustering algorithms.

Key words: k-value, initial cluster center, K-means clustering algorithm, hierarchical clustering