Shearlet Aware Prior Knowledge Feature Learning for Remote Sensing Interpretation

Key Points

• The research aims to improve remote sensing interpretation by integrating shearlet-based prior knowledge into deep learning models.
• Developed a structure-aware prior knowledge module using shearlet transforms.
• Extracted image edges and curves as structured priors.
• Embedded the module into deep learning networks to enhance modeling capabilities.
• Demonstrated improved performance in semantic segmentation and object tracking tasks.
• Achieved stable performance gains across RGB-SAR multimodal detection.
• Showed strong generalizability, accuracy, and robustness.