LAUSR

In recent years, the continuously increasing number of space objects has caught great attention and triggered the need for space surveillance for the prevention of potential space collisions. Moving objects may appear as streaks in optical images in some situations. However, most of them are usually unknown so that they lack sufficient prior information, which brings great difficulty to detection. Motivated by the fact that streaks have their unique image characteristics, this article presents a novel detection pattern for streak-like targets in single optical images. First, a space target model from point-like target to streak-like target is studied to obtain characteristics. Next, the background removal method used in software named SExtractor is applied to find target candidates. Then, an operator is used to find local maximum points in each target candidate, and the points in the same candidate are clustered according to three domains including space, intensity, and distribution, to identify whether it contains a streak. Finally, the trajectory line of each cluster is extracted. The proposed method was tested on multiple sequences of collected real optical images, which contain targets of different features and found to obtain superior performance in the probability of detection, probability of false alarm, and receiver operating characteristic (ROC), in comparison to baseline methods. In particular, the setting of thresholds is intuitive because of the designed parameters with concrete meaning in images and the normalization process.