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.

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.

The advent of Industry 4.0 has highlighted the requirements for the digitization and intelligent evolution of space-based payloads. To address challenges like limited data samples and simulate infrared images in various scenarios, this study proposes a hybrid data-driven and fractal-driven cloud-sea scenario simulation (HCSS) approach for high-precision infrared images at space-based detection scales. Static cloud-sea scenes are generated using Qilu-2 and New Technology satellite images, while dynamic scenarios are simulated with our iterative fractal dimension (FD) optimization algorithm. Next, we propose a high-precision infrared cloud-sea simulation method based on these simulate scenarios. Finally, we validate the confidence of the simulated images through morphological assessment using a two-dimensional histogram and radiative accuracy evaluation based on MODTRAN results. Experimental results confirm the method's accuracy, showing close alignment with on-orbit images. In the 2.7–3.0 ${\mu }$ m band, our average radiance is consistent with MODTRAN. Specifically, for reflection angles below 60 $^\circ$ , the root mean square error (RMSE) between our results and MODTRAN results is about 12.3% in the 3.0–5.0 ${\mu }$ m band, and around 3.7% in the 8.0–14.0 ${\mu }$ m band. Morphological assessment shows an average error of about 8.3% when compared to on-orbit images. This method allows for generating multi-band, multi-species, and multi-scale complex cloud-sea scenario images for digital infrared payloads with high flexibility and confidence.

Driven by the rising demand for digitalization and intelligent development of infrared payloads, next-generation systems must be developed within compressed timelines. High-precision digital modeling and simulation techniques offer essential data sources but often falter in complex space-based scenarios due to the limited availability of infrared characteristic data, hindering evaluation of the payload effectiveness. To address this, we propose a digital imaging simulation and verification (DISV) model for high-fidelity infrared image generation and closed-loop validation in the context of cloud–sea target detection. Based on on-orbit infrared imagery, we construct a cloud cluster database via morphological operations and generate physically consistent backgrounds through iterative optimization. The DISV model subsequently calculates scene infrared radiation, integrating radiance computations with an electron-count-based imaging model for radiance-to-grayscale conversion. Closed-loop verification via blackbody radiance inversion is performed to confirm the model’s accuracy. The mid-wave infrared (MWIR, 3–5 µm) system achieves mean square errors (RSMEs) < 0.004, peak signal-to-noise ratios (PSNRs) > 49 dB, and a structural similarity index measure (SSIM) > 0.997. The long-wave infrared (LWIR, 8–12 µm) system yields RMSEs < 0.255, PSNRs > 47 dB, and an SSIM > 0.994. Under 20–40% cloud coverage, the target radiance inversion errors remain below 4.81% and 7.30% for the MWIR and LWIR, respectively. The DISV model enables infrared image simulation across multi-domain scenarios, offering vital support for optimizing on-orbit payload performance.

With the rapid increase in the number of spacecraft and the growing complexity of the space environment, accurately characterizing and tracking space objects has become a significant challenge in ensuring space security. To address the scarcity of target characteristic data, an end-to-end infrared radiation characteristics lifecycle (E2E-IRCL) model for space targets is proposed. First, a target scenario is constructed based on motion parameters, geometric properties, and radiation physics principles. A multidimensional, omnidirectional radiation model is developed across time and space to capture dynamic variations induced by orbital motion, attitude changes, and self-movement effects. A multiband, time- and space-dependent characteristic database is subsequently established for the target. Next, a digital end-to-end infrared simulation model is developed, encompassing the full process from photon detection and photoelectric conversion to signal processing and grayscale image generation. The model enables simulation and evaluation of infrared imaging quality under varying system parameters. Finally, ground-based experimental validation demonstrated high accuracy in radiation inversion, with root mean square error values of 4.01% for the 3.7–4.8 μm band and 3.51% for the 7.7–9.3 μm band. This approach facilitates robust target characteristic modeling, supports the optimization of infrared system parameters, and enables the closed-loop design of performance metrics.

As a signature phenomenon of massive hypervelocity impacts (HVIs) in space, debris clouds provide critical optical information for satellite remote sensing and the assessment of large-scale impacts. However, studies of the optical scattering properties of debris clouds remain limited, and existing vector radiative transfer (VRT) methods struggle to accurately simulate the optical characteristics of these complex scatterers. To address this gap, this paper presents an improved Monte Carlo VRT program (PGS–MC) for multicomponent polydisperse scatterers to precisely evaluate the radiation and polarization characteristics of complex scatterers. Based on the Monte Carlo ray tracing (MCRT) method, our program introduces a particle grouping strategy (PGS) to further emphasize the importance of accounting for optical property discrepancies between different materials and particle sizes, thus significantly improving the fidelity of VRT simulations. Moreover, our program, developed using the compute unified device architecture (CUDA), can be run parallelly on graphics processing units (GPUs), which effectively reduces the computational time. The validation results indicated that the developed PGS–MC program can accurately and efficiently simulate the polarization of complex 3D scatterers. A further investigation showed that the polarization characteristics of debris clouds are highly sensitive to parameters such as the angle between the incident and detection directions, number density, particle size distribution, debris material, and wavelength. In addition, the polarization imaging of debris clouds offers distinct advantages over intensity imaging. This study offers guidance for analyzing the VRT properties of massive HVI debris clouds. Additionally, it provides a practical tool and concrete ideas for modeling the polarization characteristics of various complex scatterers, such as aircraft contrails and clouds, etc.