A Low Signal-to-Noise Ratio Infrared Small-Target Detection Network

Abstract

Space-based infrared detection technology is critical to space situational awareness, playing a significant role in noncooperative space object detection, threat perception, and space target surveillance. As space-based infrared detection technology evolves, the primary challenge is detecting more distant objects and achieving high precision in the detection of space targets with lower signal-to-noise ratios (SNRs). Owing to the scarcity of space-based data, existing methods for infrared small target detection (IRSTD) focus on high-SNR terrestrial images and perform poorly with extremely low-SNR space targets. We propose a novel low SNR space-based IRSTD network. We present a trajectory encoding enhancement module that uses multiframe data to accumulate energy along the target's trajectory. It leverages multiframe temporal information, effectively enhancing the target while suppressing the background. This module can be integrated into most single-frame target detection networks. Additionally, we combine residual networks with global context aggregation to enhance the network's ability to extract features from small infrared targets. In the feature fusion phase, we propose a multiscale perception fusion module that expands the receptive field of shallow features and integrates multiscale information to accurately detect targets. We conduct extensive validation on real infrared space target datasets and semisimulated datasets, and our approach achieves the best performance. For targets with an SNR of 0.7, over 97% detection and fewer than 10–6 false alarms are achieved.

Keywords

• Deep learning (DL)
• low signal-to-noise ratio (SNR) target detection
• space-based infrared detection
• spatial–temporal feature