适用于大视场的快速星像匹配算法
A Fast Catalogue Matching Algorithm for Large Field of View
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摘要: 基于CCD图像的天文观测中,星像匹配是一项基本的任务。提出一种基于k-d树和k-means聚类算法的星像匹配算法,应用三角形不变性元组对相似三角形进行盲匹配,算法可以间接计算CCD图像的比例尺。三次使用k-d树优化计算,并使用k-means聚类算法对图像进行分割,提高星像匹配的精度。使用云南天文台1 m望远镜拍摄的稀疏星场和2.4 m望远镜拍摄的密集星场进行了星像匹配算法的测试。实验结果表明,该方法能较好地自适应图像比例尺的微小变化,同时提高星像匹配的精度。Abstract: Stellar catalogue matching is a fundamental task in astronomical observations based on CCD images. In this paper, a stellar catalogue matching algorithm based on k-d tree and k-means clustering algorithm is proposed in Python. Triangle invariant tuples are used for blind matching of similar triangles based on Astroalign, which can indirectly calculate the scale of CCD image. In this paper, k-d tree is used for three times to optimize the calculation, and k-means clustering algorithm is used to segment the image to improve the matching precision. For testing, we match the stars with both sparse field frames taken by the 1 m telescope at Yunnan Observatory and dense field frames taken by the 2.4 m telescope at Yunnan Observatory. Our results show that the method can effectively adapt to subtle change of image scale and improve matching precision.
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