Ship target detection faces the challenges of complex and changing environments combined with the varied characteristics of ship targets. In practical applications, the complexity of meteorological conditions, uncertainty of lighting, and the diversity of ship target characteristics can affect the accuracy and efficiency of ship target detection algorithms. Most existing target detection methods perform well in conditions of a general scenario but underperform in complex conditions. In this study, a collaborative network for target detection under foggy weather conditions is proposed, aiming to achieve improved accuracy while satisfying the need for real-time detection. First, a collaborative block was designed and SCConv and PCA modules were introduced to enhance the detection of low-quality images. Second, the PAN + FPN structure was adopted to take full advantage of its lightweight and efficient features. Finally, four detection heads were used to enhance the performance. In addition to this, a dataset for foggy ship detection was constructed based on ShipRSImageNet, and the mAP on the dataset reached 48.7%. The detection speed reached 33.3 frames per second (FPS), which is ultimately comparable to YOLOF. It shows that the model proposed has good detection effectiveness for remote sensing ship images during low-contrast foggy days.

Temperature field calculation is an important step in infrared image simulation. However, the existing solutions, such as heat conduction modelling and pre-generated lookup tables based on temperature calculation tools, are difficult to meet the requirements of high-performance simulation of infrared images based on three-dimensional scenes under multi-environmental conditions in terms of accuracy, timeliness, and flexibility. In recent years, machine learning-based temperature field prediction methods have been proposed, but these methods only consider the influence of meteorological parameters on the temperature value, while not considering the geometric structure and the thermophysical parameters of the object, which results in the low accuracy. In this paper, a multivariate temperature field prediction network based on heterogeneous data (MTPHNet) is proposed. The network fuses geometry structure, meteorological, and thermophysical parameters to predict temperature. First, a Point Cloud Feature Extraction Module and Environmental Data Mapping Module are used to extract geometric information, thermophysical, and meteorological features. The extracted features are fused by the Data Fusion Module for temperature field prediction. Experiment results show that MTPHNet significantly improves the prediction accuracy of the temperature field. Compared with the v-Support Vector Regression and the combined back-propagation neural network, the mean absolute error and root mean square error of MTPHNet are reduced by at least 23.4% and 27.7%, respectively, while the R-square is increased by at least 5.85%. MTPHNet also achieves good results in multi-target and complex target temperature field prediction tasks. These results validate the effectiveness of the proposed method.

Remote sensing images (RSIs) are limited by low-resolution imaging and a variety of degradation effects, posing significant challenges for high-level visual tasks. Traditional deep learning-based super-resolution (SR) algorithms struggle to optimize these issues end-to-end, which inhibits the algorithm’s lightweight design and restricts its application in real-time scenarios. To address this, we introduce a multi-branch attention integration network (MAIN) for the degraded RSI SR. This network features two key functional components: the multi-scale feature perception (MsFP) module and the multi-branch attention integration block (MAIB). The MsFP module, a lightweight convolution-based architecture, is designed primarily to counteract degradation effects and depict low-dimensional features. MAIB, a multi-branch parallel layout, employs attention mechanisms to enable high-level semantic information extraction and context-aware learning. Furthermore, we utilize modulation transfer functions to emulate various degradation effects, thus creating a dedicated dataset for degraded RSI SR, called DeRSSA. Extensive experimental evidence shows that MAIN exceeds the performance of the existing state-of-the-art method for the degraded RSI SR task. Our code and dataset will be accessible at https://github.com/lbo0928/MAIN.

High-precision 3D reconstruction of on-orbit non-cooperative targets is essential for space situational awareness. However, extreme space environments induce severe imaging degradations, including high-dynamic-range (HDR) illumination, rapid motion blur, and platform jitter. Traditional 3D Gaussian Splatting (3DGS) conflates these optical distortions with geometric optimization, leading to pathological structural inflation and the loss of thin appendages like solar panels. To overcome this, we propose OrbitGS, a physically decoupled 3DGS framework. OrbitGS integrates physical imaging priors via a Kinematics-Driven Degradation Synthesizer (KDDS) to deterministically extract view-specific degradation kernels. Furthermore, a blur-decoupled rendering strategy with intensity-aware weighting mitigates HDR variations, while a semantic-aware densification scheme mathematically penalizes abnormal primitive expansion. Evaluations on the SPE3R dataset demonstrate that OrbitGS effectively disentangles optical degradations from the geometric representation. Quantitatively, evaluated across seven space targets under moderate (200-view) and extreme sparse (50-view) settings, our framework achieves state-of-the-art robustness against extreme degradations. Notably, it avoids the catastrophic structural blow-ups observed in baseline methods, yielding an average geometric F1-score of 0.80 and a Chamfer Distance of 0.818, alongside a rendering Structural Similarity Index (SSIM) of 0.82 and a Learned Perceptual Image Patch Similarity (LPIPS) of 0.16. By preserving delicate structures under severe degradation, OrbitGS provides a robust, high-fidelity 3D reconstruction solution for complex orbital environments.