A Robust Space Target Detection Algorithm Based on Target Characteristics

In space-based observations, there are large numbers of targets with large size ranges and intensities in a given image. However, the current detection studies mostly emphasize the saliency of targets but neglect their distribution characteristics, which may result in incorrect or missed detection results. In this letter, an effective detection algorithm based on target characteristics is proposed to pursue good multitarget detection performance. First, a space target model is studied to obtain its characteristics. Next, operators are applied to reduce the influence and search for suspected target-center points. Then, local regions around these points are extracted and normalized to reduce the intensity differences among targets and the influence of light conditions. Finally, three features are designed to confirm these suspected regions and their scale information. Furthermore, the threshold setting process is intuitive and significant while keeping the developed method robust in different cases. Compared with other current algorithms, the proposed algorithm achieves superior detection performance in terms of the number of detected targets, accuracy rate, false alarm rate, and computational cost.