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.

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.

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.

Space resources are of economic and strategic value. Infrared (IR) remote sensing, unaffected by geography and weather, is widely used in weather forecasting and defense. However, detecting small IR targets is challenging due to their small size and low signal-to-noise ratio, and the resulting low detection rates (DRs) and high false alarm rates (FRs). Existing algorithms struggle with complex backgrounds and clutter interference. This paper proposes an adaptive differential event detection method for space-based aerial target observation, tailored to the characteristics of target motion. The proposed IR differential event detection mechanism uses trigger rate feedback to dynamically adjust thresholds for strong, dynamic radiation backgrounds. To accurately extract targets from event frames, a lightweight target detection network is designed, incorporating an Event Conversion and Temporal Enhancement (ECTE) block, a Spatial-Frequency Domain Fusion (SFDF) block, and a Joint Spatial-Channel Attention (JSCA) block. Extensive experiments on simulated and real datasets demonstrate that the method outperforms state-of-the-art algorithms. To advance research on IR event frames, this paper introduces SITP-QLEF, the first remote-sensing IR event dataset designed for dim and moving target detection. The algorithm achieves an mAP@0.5 of 96.3%, an FR of 4.3 $\times {10}^{-5}$, and a DR of 97.5% on the SITP-QLEF dataset, proving the feasibility of event detection for small targets in strong background scenarios.

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.