Various gases and aerosols in bad weather conditions can cause severe image degradation, which will seriously affect the detection efficiency of optical monitoring stations for high pollutant discharge systems. Thus, penetrating various gases and aerosols to sense and detect the discharge of pollutants plays an important role in the pollutant emission detection system. Against this backdrop, we recommend a real-time optical monitoring system based on the Stokes vectors through analyzing the scattering characteristics and polarization characteristics of both gases and aerosols in the atmosphere. This system is immune to the effects of various gases and aerosols on the target to be detected and achieves the purpose of real-time sensing and detection of high pollutant discharge systems under bad weather conditions. The imaging system is composed of four polarizers with different polarization directions integrated into independent cameras aligned parallel to the optical axis in order to acquire the Stokes vectors from various polarized azimuth images. Our results show that this approach achieves high-contrast and high-definition images in real time without the loss of spatial resolution in comparison with the performance of conventional imaging techniques.

With the rapid development of the virtual reality industry, one of the bottlenecks is the scarcity of video resources. How to capture high-definition panoramic video with depth information and real-time stereo display has become a key technical problem to be solved. In this paper, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed. Combined with the real-time processing algorithm of multi detector mosaic panoramic stereo imaging image, a panoramic stereo real-time imaging system is developed. Firstly, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed, and the space coordinate calibration platform of ultra-high precision panoramic camera based on theodolite angle compensation function is constructed. The projection matrix of adjacent cameras is obtained by solving the imaging principle of binocular stereo vision. Then, a real-time registration algorithm of multi-detector mosaic image and Lucas-Kanade optical flow method based on image segmentation are proposed to realize stereo matching and depth information estimation of panoramic imaging, and the estimation results are analyzed effectively. Experimental results show that the stereo matching time of panoramic imaging is 30 ms, the registration accuracy is 0.1 pixel, the edge information of depth map is clearer, and it can meet the imaging requirements of different lighting conditions.

Change detection (CD) in high-resolution remote sensing imagery remains challenging due to the complex nature of objects and varying spectral characteristics across different times and locations. Convolutional neural networks (CNNs) have shown promising performance in CD tasks by extracting meaningful semantic features. However, traditional 2D-CNNs may struggle to accurately integrate deep features from multi-temporal images, limiting their ability to improve CD accuracy. This study proposes a Multi-level Feature Cross-Fusion (MFCF) network with 3D-CNNs for remote sensing image change detection. The network aims to effectively extract and fuse deep features from multi-temporal images to identify surface changes. To bridge the semantic gap between high-level and low-level features, a MFCF module is introduced. A channel attention mechanism (CAM) is also integrated to enhance model performance, interpretability, and generalization capabilities. The proposed methodology is validated on the LEVIR construction dataset (LEVIR-CD). The experimental results demonstrate superior performance compared to the current state-of-the-art in evaluation metrics including recall, F1 score, and IOU. The MFCF network, which combines 3D-CNNs and a CAM, effectively utilizes multi-temporal information and deep feature fusion, resulting in precise and reliable change detection in remote sensing imagery. This study significantly contributes to the advancement of change detection methods, facilitating more efficient management and decision making across various domains such as urban planning, natural resource management, and environmental monitoring.

In order to achieve a clear observation of the ultra-low brightness solar corona and provide a physical basis for forecasting space weather that seriously affects the human living environment, the stray-light suppression level becomes the key factor affecting the development of the coronagraph. In this study, a stray-light suppression method is adopted for Solar Corona Imager (SCI) which is a dual-waveband internally occulted reflecting coronagraph simultaneously and independently observing the inner corona in the HI Lyman-alpha (121.6 ± 10 nm) line and white-light (700.0 ± 40 nm) wavebands with a field-of-view (FOV) from 1.1 to 2.5 R_⊙ (R_⊙ stands for the mean solar radius). The scattered stray-light from the primary mirror, including the surface errors, cosmetic defects, and particulate contamination, is analyzed and suppressed, and the corresponding scattering models are established for simulation based on the laboratory testing. The stray-light measurement results for SCI in the laboratory show that the stray-light level can be suppressed to the order of 10⁻⁸ B_⊙ at 2.5 R_⊙ (B_⊙ is the mean brightness of the solar disk) in the white-light (WL) band, which is consistent with the stray-light level obtained by simulation and verifies the modeling and simulation.

Extreme ultraviolet (EUV) imagers are key tools to monitor the space environment and forecast space weather. EUV filters are important components to block radiation in the ultraviolet (UV), visible, and near-infrared (IR) regions. In this study, various characterization methods were proposed for the nickel mesh-supported indium (In) filter, and their spectral characteristics were comprehensively studied. The material and thickness of the filter were chosen based on atomic scattering principles, determined through theoretical calculation and software simulation. The metal film was deposited using the vacuum-resistive thermal evaporation method. The measured transmission of the filter was 10.06% at 83.4 nm. The surface elements of the sample were analyzed using X-ray photoelectron spectroscopy (XPS). The surface and cross-sectional morphologies of the filter were observed using a scanning electron microscope (SEM). The impact of the oxide layer and carbon contamination on the filter’s transmittance was investigated using an ellipsometer. A multilayer “In-In₂O₃-C” model was established to determine the thickness of both the oxide layer and carbon contamination layer on the filter. This model introduces the filling factor based on the original model and considers the diffusion of the contamination layer, resulting in more accurate fitting results. The transmittance of the filter in the visible light range was measured using a UV-VIS spectrophotometer, and the measurement error was analyzed. This article provides preparation methods and test methods for the 83.4 nm EUV filter and conducts a detailed analysis of the spectral characteristics of the prepared optical filters, which hold significant value for space exploration applications.