Multi-Grained Global-Local Semantic Feature Fusion for Few Shot Remote Sensing Scene Classification

Few-shot remote sensing scene classification aims to classify unseen scenes by using only a few labeled samples. Hence, how to set up a more effective feature description according to a few labeled samples, becomes an important issue. In this paper, in view of more complicated remote sensing scenes containing several hierarchical and coupled spatial relations (e.g., internal and external spatial contexts), which severely hinder the feature extraction under few-shot learning scenarios, a multi-grained global-local semantic feature fusion (MGGL-SFF) method is proposed for few-shot remote sensing scene classification, which can better combine the global discriminative spatial semantic features with local transferable fragment features to set a powerful prototype representation up for few shot learning. Finally, experiments are carried out on defined few-shot remote sensing scene classification benchmark, and results proved the proposed MGGL-SFF can achieve a new state-of-the-art performance.