HAM: Hierarchical Attention Mamba With Spatial–Frequency Fusion for Remote Sensing Image Super-Resolution

Vision Mamba shows significant potential for enhancing remote sensing image super-resolution (RSISR) performance due to its linear-complexity global modeling capabilities. However, existing Mamba-based RSISR models face two key limitations in handling remote sensing imagery. First, insufficient hierarchical feature interaction hinders the capture of multiscale spatial structures, which are crucial for reconstructing diverse patterns and fine textures. Second, inadequate local–global correlation extraction weakens the restoration of high-frequency details in conjunction with broader contextual information, ultimately limiting spatial consistency and image fidelity. To address these issues, we propose the hierarchical attention Mamba (HAM) network with spatial–frequency feature enhancement for RSISR. Key innovations include the hierarchical aggregation attention (HAA) module, which enhances the propagation and utilization of multilevel features, and the spatial–frequency information interaction module (SFIIM), which facilitates the fusion of spatial–frequency features. The SFIIM employs the dynamic frequency spectrum enhancement (DFSE) technique for discriminative feature learning through Fourier amplitude–phase decoupling, along with the hybrid spatial feature aggregator (HSFA), which utilizes a spatial-domain dual-branch channel-wise gating mechanism. Together, these techniques significantly enhance the model’s ability for synchronous local–global feature extraction. Extensive evaluations across six RSISR benchmarks (including AID, DIOR, and DOTA) demonstrate that HAM achieves state-of-the-art performance, delivering superior image reconstruction quality while maintaining computational efficiency.