Advances and Challenges in Solar Flare Prediction: A Review

To address the critical detection challenges in Satellite Laser Ranging (SLR) observations— including random noise, fixed-pattern interference (e.g., hot pixels), dynamic trajectory confusion, strong signal masking, and low grayscale contrast—this paper proposes a weak target detection algorithm for SLR videos based on the fusion of spatial and temporal variance. First, a comprehensive preprocessing module is constructed, incorporating bad pixel mask restoration and column-wise statistical differencing to concurrently eliminate intrinsic sensor hot pixels and vertical stripe noise; meanwhile, a non-linear square mapping is employed to enhance the grayscale contrast of weak targets. Second, a dual-channel detection mechanism is designed by leveraging the complementary characteristics of spatio-temporal variance: the spatial variance channel utilizes temporal mean and spatial variance to jointly accumulate weak signals, addressing the invisibility issue caused by single-frame noise; the temporal variance channel utilizes the temporal fluctuation features of pixel intensity to physically distinguish moving targets from static high-brightness interference, thereby resolving the indistinguishability problem. Finally, the strong signal masking effect is effectively overcome through the fusion of spatio-temporal variance features and a dynamic saturation mechanism based on the noise floor. Extensive tests on 92 real observation videos involving 12 types of satellites demonstrate that the proposed algorithm achieves a high detection rate of 95.6% while suppressing the average number of false alarms to 0.03. The experimental results indicate that the algorithm can effectively mitigate the effects of strong interference and low contrast, achieving high-precision localization and extremely low false-alarm detection for weak SLR targets.