Masked Second-Order Pooling for Few-Shot Remote-Sensing Scene Classification

Few-shot remote-sensing scene classification (FSRSSC) is the task of categorizing remote-sensing images (RSIs) with insufficient labeled samples. This task becomes particularly challenging due to low intraclass similarity and high interclass similarity in RSIs. To overcome these challenges, we introduce a novel masked second-order pooling (MSoP) module to exploit the masked second-order features, enhancing feature representation and classification performance for FSRSSC. The MSoP module comprises two key components: a learnable DropBlock (LDB) and a compressed second-order pooling (CSoP). The LDB selectively masks discriminative regions in feature maps, effectively alleviating the low intraclass similarity problem. On the other hand, the CSoP enhances second-order statistics by computing and aggregating channel-wise similarity, thereby reducing the impact of interclass similarity. We construct an MSoP-Net based on the proposed MSoP module. Experimental results demonstrate its superior performance on two widely used datasets, that is, NWPU-RESISC45 and UCM.