Remote Sensing Scene Classification via Pseudo-Category-Relationand Orthogonal Feature Learning

Remote sensing (RS) scene classification is a crucial component in the analysis of Earth observation data, aiding in a deeper understanding and monitoring of our dynamic planet. Its applications extend across various fields, including land management, urban analysis, and environmental monitoring. The complex semantic information in RS scene images and the relationships between different scene categories present significant challenges to improving scene classification tasks. Unlike previous methods that only focus on network structure or feature encoding, our approach emphasizes the association of scene categories, integrating feature learning and knowledge transfer together to enhance the analysis of scenes at a higher semantic level. To this end, we propose an RS scene classification scheme based on pseudo-scene category-relation reasoning and orthogonal feature (OF) learning modules, capturing the inherent semantic connections among diverse scene classes. Additionally, we introduce cascaded attention (CA) and selected separation modules to strategically optimize the network, targeting challenging classes with high feature similarities. Knowledge is then distilled across different branches, guiding to enhance the model’s robustness and prediction accuracy. Experiments are conducted on three challenging RS scene datasets of AID30, UCMerced21, and NWPU-RESISC45 to validate the effectiveness of the learned pseudo-category relationships. The results demonstrate that the proposed framework outperforms existing hierarchical approaches in leveraging the hierarchical structure of RS scene images.