FCD-Mamba: A Frequency-Enhanced and Center-Pixel-Guided Dual-Branch Mamba Network for Hyperspectral Image Classification

Abstract

Hyperspectral image classification is an important task in remote sensing image interpretation. However, existing Mamba-based methods remain limited in exploiting shallow frequency information, preserving neighborhood continuity during spatial serialization, and protecting center-pixel semantics. To address these limitations, we propose a frequency-enhanced and center-pixel-guided dual-branch Mamba network (FCD-Mamba). First, the Frequency-Enhanced Multiscale Convolution Module (FEMSCM) applies serial Fourier filtering to enhance multiscale spatial features and calibrates spectral features using low-, middle-, and high-frequency DCT energy descriptors derived from the original spectrum of the center pixel. Second, the Spatial Center-Adaptive Mamba (SCA-Mamba) employs two orthogonal Hilbert curves and their reverse traversals to preserve spatial neighborhood continuity, while adaptively aggregating multi-path features using center and global representations. The Spectral Center-Preserving Mamba (SCP-Mamba) constructs center-preserved and global spectral sequences and models their complementary spectral dependencies through bidirectional scanning. Finally, the Center-Pixel-Guided Residual Fusion Module (CGRFM) exploits center-to-global discrepancies to calibrate dual-branch features and introduces auxiliary supervision to maintain the discriminability of each branch. Experiments on multiple public hyperspectral datasets demonstrate that FCD-Mamba achieves stable and competitive classification performance, validating the effectiveness of its constituent modules.