MPT-SFANet: Multi-Order Pooling Transformer-Based Semantic Feature Aggregation Network for SAR Image Classification

The transformer-based methods have demonstrated remarkable advancements in synthetic aperture radar (SAR) classification. Nevertheless, many of these methods ignore global statistical information and semantic feature interaction for effectively characterizing different SAR land covers under complex structure. Leveraging second-order statistics presents an efficacious approach to characterize statistical features of SAR patches well. Motivated by this, we integrate pyramid pooling and global covariance pooling techniques into each of the multi-head self-attention (MHSA) blocks, thereby facilitating the extraction of powerful contextual features and global statistical nature of SAR patches, namely multi-order pooling transformer module (MPTM). Simultaneously, a semantic feature aggregation module (SFAM) is utilized for capturing local deep features and modeling the interaction of feature information across various feature levels. Both these modules are embedded into a U-shaped architecture, which we refer to as multi-order pooling transformer-based semantic feature aggregation network (MPT-SFANet). Through extensive experimental results on TerraSAR, Sentinel-1B, and GF-3 SAR image classification datasets indicate that MPT-SFANet exceeds several relevant methods.