Spatial-temporal local contrast for moving point target detection in space-based infrared imaging system

• Published in: Infrared physics & technology Vol. 95; pp. 53 - 60
• Main Authors: Zhao, Bendong, Xiao, Shanzhu, Lu, Huanzhang, Wu, Dongya
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2018
• Subjects: Moving point target detection; Space-based IR imaging system; Spatial-temporal local contrast; Time domain difference; Space-based IR imaging system; Spatial-temporal local contrast; Moving point target detection; Time domain difference
• ISSN: 1350-4495; 1879-0275
• DOI: 10.1016/j.infrared.2018.10.011

•A simple but effective spatial filter is proposed to obtain the SLCM.•An enhanced time difference method is proposed to obtain the TLCM.•The STLCM is defined by multiplying the SLCM and TLCM.•An enhanced threshold segmentation method is proposed. Infrared (IR) moving point target detection is an important technique in many onboard applications such as remote sensing, IR searching and tracking (IRST) and early warning system. However, it has been facing great challenges due to the complicated background and the limited processing resources in the onboard system. In this paper, a novel spatial-temporal local contrast method is proposed for moving point target detection in space-based IR imaging system. Firstly, a simple but effective spatial filter based on multi-direction filtering fusion is designed to obtain the spatial local contrast map (SLCM). An enhanced time domain difference method is also proposed to obtain the temporal local contrast map (TLCM). Then, the spatial–temporal local contrast map (STLCM) is obtained by multiplying the newly defined SLCM and TLCM. Finally, an enhanced threshold segmentation method is proposed for target detection and false alarm suppression. To verify the performance of our detection algorithm, we conduct several groups of experiments on four different real IR image sequences with simulated targets. The final experimental results show that our algorithm significantly outperforms other methods in terms of background suppression and target detection.