LAUSR

Abstract In most large field of view (FOV) observations, the distortion problem is inevitably and significantly more serious than in small FOV ones. In the circumstances, many traditional star identification approaches are not able to efficiently identify stars any more. In order to deal with this problem, we put forward a star identification method that is less sensitive to distortion. The method first processes stars in the central area of the image, using traditional identification logic, and then applies the region growing strategy to enlarge the identified regions iteratively until the entire image is covered. The performance of the new scheme is analysed in the presence of both simulated data and real data. The results show that the proposed algorithm has the advantage of speed, and the strategy of regional extension can efficiently identify stars in large FOV images compared with other existing algorithms.