Centroid extraction from star images is a critical component in achieving high-precision satellite attitude determination. Prevailing approaches primarily focus on suppressing a single type of error or depend on fixed filtering and compensation parameters, often lacking a multidimensional and fine-grained analysis and handling of diverse error sources. To address these limitations, this paper proposes a compensation method for centroid extraction based on error classification and modeling, coupled with an adaptive strategy selection mechanism to improve accuracy. Experimental results demonstrate the efficacy of the proposed method: on a set of 30 to 300 laboratory-simulated star images, it enhanced the average centroid extraction accuracy from a baseline of 0.31–0.45 pixels to 0.11–0.19 pixels when using a Static model Unscented Kalman Filter (UKF) integrated with four sub-pixel interpolation techniques. Furthermore, for a larger dataset of 300 to 600 star images simulated at a 300 Hz frame rate, the method achieved an accuracy improvement exceeding 50% across five different motion model UKF methods, demonstrating robust performance.