Detection of small space target based on iterative distance classification and trajectory association

To realize automatic target detection, an algorithm is proposed to detect small visible optical space targets against low SNR conditions. Firstly, the single-frame image background is segmented, and the segmentation coefficient is determined by a Constant False Alarm Ratio (CFAR) method. Then, a feature space is formed based on structural stability of the star, and classification criterion function is constructed for the distance feature space. Furthermore, candidate targets are extracted by using the iterative optimization distance classification method. Finally, small visible optical space targets are detected by trajectory association based on the continuity of target motion. In addition, an evaluation method combined with single frame detection probability, single frame false alarm probability and sequence detection probability is proposed. Experimental results indicate that the detection probability of sequence is more than 96.02%, and the false alarm probability is less than 4.4% when the SNR≤3.It concludes that the method can promote the detection probability against low SNR conditions significantly, and can remove the false alarm effectively.