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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.