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.

Accurate quality assessment of solar Extreme Ultraviolet (EUV) remote sensing imagery is critical for data reliability in space science and weather forecasting. This study introduces a hybrid framework that fuses deep semantic features from a HyperNet-based model with 22 handcrafted physical and statistical quality indicators to create a robust 24-dimensional feature vector. We used a dataset of top-quality images, i.e., quality class “Excellent”, and generated a dataset of 47,950 degraded, lower-quality images by simulating seven types of degradation including defocus, blur and noise. Experimental results show that an XGBoost classifier, when trained on these fused features, achieved superior performance with 97.91% accuracy and an AUC of 0.9992. This approach demonstrates that combining deep and handcrafted features significantly enhances the classification’s robustness and offers a scalable solution for automated quality control in solar EUV observation pipelines.