Signal-to-noise analysis of point target detection using image pixel binning for space-based infrared electro-optical systems

•The applicability of binning as a technique that can enhance SNR has not been fully discussed in space-based detection. Unlike static scenarios, the dynamic scenarios of space-based point target detection are more complex and no theoretical derivation of the SNR enhancement degree can be given. In this paper, we simulate the database under different scenarios by building a detection model and give the analysis of SNR enhancement under different detection scenarios.•A space-based detection model is developed to analyze the binning performance under different system characteristics and detection scenarios. The model is also validated using the measured 3483 point target data.•We implement sub-pixel segmentation and sub-integral time segmentation to accumulate the energy of the target on the detection system. This enables SNR analysis in dynamic scenes. In addition, intra-pixel sensitivity is considered for better conformity with the actual system imaging and to improve the model accuracy. A point target detection database with 772,800 different detection scenes is established with high adaptability and robustness.•The mean error of SNR lift between the model and experimental data was 0.016. verifying the correctness and high accuracy of the theoretical analysis of the proposed model.•It is of theoretical guidance for the design of parameters of space-based infrared optoelectronic systems. And the model can be analyzed for the detection parameter setting of any existing system. In low SNR detection imaging, binning is used to enhance the detection performance more effectively. It is well known that pixel binning is an effective method for improving the signal-to-noise ratio (SNR) of silicon detectors. It has found extensive use in the study of spectroscopy, astronomy, and in the development of numerous image processing algorithms. However, the positive impact of binning technique on SNR does not happen at any moment. In this paper, we model the scenario of the detection of the point target. Our study analyzes the effects of variables such as encircled energy (EE), target motion velocity, and integration time on the imaging process. For different detection scenarios, we quantitatively discussed the degree of improvement in SNR of image pixel binning technique for detecting point targets. Based on actual 3483 point target imaging data collected, the model was validated with an average error of 0.016. A wide range of detection scenarios is covered by the propos