Significant radiometric discrepancies commonly exist among multisource optical satellite images due to differences in sensor characteristics, acquisition geometry, atmospheric conditions, and imaging time, which severely limit their application accuracy in land-cover classification, change detection, and geophysical parameter retrieval. Therefore, relative radiometric normalization (RRN) has become an essential preprocessing step for the integrated application of multisource optical satellite imagery. However, existing RRN methods often suffer from low automation in radiometric reference sample selection and are highly sensitive to anomalous samples, resulting in limited normalization accuracy and robustness. To address these issues, this study proposes an automated cross-sensor RRN framework based on automatic construction and intelligent refinement of radiometric reference sample set (RRSS). An unsupervised deep-learning-based change detection approach is employed to automatically identify radiometrically stable samples and construct an initial RRSS. Subsequently, a dual-stage sample refinement strategy integrating statistical outlier detection and topographic prior knowledge is developed to progressively remove anomalous samples and improve sample reliability. Linear regression is then performed using the refined samples to establish the radiometric normalization model. Experiments conducted on multisource optical satellite imagery acquired under different terrain conditions demonstrate that the proposed framework consistently outperforms five mainstream RRN methods, achieving lower Root Mean Square Error (RMSE) and higher Spectral Angle Cosine (SAC) and Structural Similarity Index Measure (SSIM) values. The results indicate that automatic construction and intelligent refinement of RRSS effectively improve inter-image radiometric consistency, particularly in topographically complex regions. Root Mean Square Error (RMSE), Spectral Angle Cosine (SAC), and Structural Similarity Index Measure (SSIM).