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-signal-to-noise ratio space-based infrared small target detection network (LSTD-Net). We present a trajectory encoding enhancement module (TEEM) 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 (MPFM) 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. Finally, we provide a small, accurately labeled, semisimulated multiframe infrared deep-space background low-SNR small target dataset, which is available at https://github.com/lifenghong/SatMIR.

Low signal-to-noise ratio (SNR) infrared point target detection and tracking is crucial to study regarding infrared remote sensing. In the low-SNR images, the intensive noise will submerge targets. In this letter, a saliency-guided double-stage particle filter (SGDS-PF) formed by the searching particle filter (PF) and tracking PF is proposed to detect and track targets. Before the searching PF, to suppress noise and enhance targets, the single-frame and multi-frame target accumulation methods are introduced. Besides, the likelihood estimation filter and image block segmentation are proposed to extract the likelihood saliency and obtain proper proposal density. Guided by this proposal density, the searching PF detects potential targets efficiently. Then, with the result of the searching PF, the tracking PF is adopted to track and confirm the potential targets. Finally, the path of the real targets will be output. Compared with the existing methods, the SGDS-PF optimizes the proposal density for low-SNR images. Using a few accurate particles, the searching PF detects potential targets quickly and accurately. In addition, initialized by the searching PF, the tracking PF can keep tracking targets using very few particles even under intensive noise. Furthermore, the parameters have been selected appropriately through experiments. Extensive experimental results show that the SGDS-PF has an outstanding performance in tracking precision, tracking reliability, and time consumption. The SGDS-PF outperforms the other advanced methods.

Space-based target surveillance is important for aerospace safety. However, with the increasing complexity of the space environment, the stellar target and strong noise interference pose difficulties for space target detection. Simultaneously, it is hard to balance real-time processing with computational performance for the onboard processing platform owing to resource limitations. The heterogeneous multi-core architecture has corresponding processing capabilities, providing a hardware implementation platform with real-time and computational performance for space-based applications. This paper first developed a multi-stage joint detection and tracking model (MJDTM) for space targets in optical image sequences. This model combined an improved local contrast method and the Kalman filter to detect and track the potential targets and use differences in movement status to suppress the stellar targets. Then, a heterogeneous multi-core processing system based on a field-programmable gate array (FPGA) and digital signal processor (DSP) was established as the space-based image processing system. Finally, MJDTM was optimized and implemented on the above image processing system. The experiments conducted with simulated and actual image sequences examine the accuracy and efficiency of the MJDTM, which has a 95% detection probability while the false alarm rate is 10⁻⁴. According to the experimental results, the algorithm hardware implementation can detect targets in an image with 1024 × 1024 pixels in just 22.064 ms, which satisfies the real-time requirements of space-based surveillance.

Using infrared technology to accurately detect small weak targets is crucial in various fields, such as reconnaissance and security. However, the infrared detection of small weak targets is challenged by complex backgrounds, tiny target sizes, and low signal-to-noise ratios, which significantly increase the difficulty of detection. Early studies in this domain typically utilized manually designed feature-extraction methods that performed inadequately in the presence of complex backgrounds. While advancements in deep learning have spurred rapid progress in this field, with CNN models effectively enhancing the detection performance, the problem of small weak target features being lost persists. HMCNet, which employs a hybrid architecture combining a state space model and a CNN, is proposed in this paper; its hybrid architecture demonstrates the capacity to extract the local features and model the global context, facilitating superior suppression of complex backgrounds and detection of small weak targets. Our experimental results on the public IRSTD-1k dataset and our own MISTD dataset indicate that, compared to the current mainstream methods, the method proposed achieves better detection accuracy while maintaining high-speed inference capabilities, thus validating the rationality and effectiveness of this research.

Performing moving target detection in space-based infrared detection systems has long been a critical challenge and research focus. The primary difficulties stem from the small and textureless nature of targets, complex backgrounds, and substantial noise. Additionally, satellite platform mobility causes the backgrounds to move, further complicating the task of distinguishing moving targets from dynamic clutter. Moreover, the lack of publicly available multiframe moving target datasets derived from space-based platforms has hindered the development of space-based infrared moving target detection technologies. Therefore, a moving target dataset (MIRSat-QL) with a complex dynamic background was constructed from a space-based low-Earth orbit satellite platform. A novel motion feature enhancement-based dynamic infrared space target detection network (MFE-Net) is subsequently proposed. Specifically, MIRSat-QL is a semisimulated dataset that combines space-based low-Earth-orbit satellite imagery with ground-truth target data, providing reliable data support for detecting dynamic targets in complex space-based scenarios and facilitating algorithmic development and validation. To address detection challenges and reduce false alarms caused by dynamic clutter interference in complex scenes, a motion-guided feature refinement (MGFR) module is introduced. This module effectively extracts motion features, suppresses dynamic backgrounds, and enhances target signals via dynamic background correspondence mapping (DBCM) and multiframe differential enhancement (MDE). Additionally, a multiattention-guided feature fusion (AGFF) module, which efficiently performs dynamically weighted fusion and multiscale feature refinement, is designed, significantly enhancing the ability of the model to detect small targets. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art (SOTA) performance. The dataset and code are available at https://github.com/lifenghong/MFE-Net.

Human space exploration has brought a growing crowded operating environment for in-orbit spacecraft. Monitoring the space environment and detecting space targets with photoelectric equipment has extensive and realistic significance in space safety. In this study, a local spatial–temporal registration (LSTR) method is proposed to detect moving small targets in space. Firstly, we applied the local region registration to estimate the neighbor background motion model. Secondly, we analyzed the temporal local grayscale difference between the strong clutter and target region and measured the temporal local–central region difference to enhance the target. Then, the temporal pixel contrast map was calculated, which further retains the target signal and suppresses the residue clutter. Finally, a simple adaptive threshold segmentation algorithm was applied to the saliency map to segment the targets. Comparative experiments were conducted on four groups of image sequences to validate the efficiency and robustness of the algorithm. The experimental findings indicate that the proposed method performs well in target enhancement and clutter suppression under different scenarios.