Wavelet Decomposition-Based Spectral–Spatial Mamba Network for Hyperspectral Image Classification

Existing hyperspectral image classification (HSIC) models based on the Mamba architecture predominantly center on characterizing the original spectral and spatial domains, with limited exploration of time–frequency analysis. In this study, we propose a novel wavelet decomposition-based spectral–spatial Mamba network for HSIC, dubbed “WD-SSMamba.” This model incorporates both 1-D and 2-D wavelet decompositions to extract spectral and spatial features in the frequency domain, respectively. Specifically, we design an innovative frequency feature extraction (FE) block, which comprises a spectral wavelet convolution (SWC) module for spectral FE and a wavelet separable convolution (WSC) module for spatial FE. Furthermore, to address the challenges of integrating spectral–spatial features in traditional Mamba models for HSIC, we devise a dual-branch Mamba block featuring a cross-fusion structure, termed the “HyperMamba” block, to efficiently extract and fuse spectral and spatial features. Comprehensive experiments were conducted on four publicly available hyperspectral image (HSI) datasets, namely, Pavia University, WHU-Hi-LongKou, WHU-Hi-HongHu, and Houston 2013. The results show that the WD-SSMamba model achieves overall accuracies (OAs) of 92.48%, 97.91%, 89.98%, and 87.00% on these datasets, respectively, with fewer than 20 training samples per class, surpassing those of other competing models across all tested datasets. Moreover, it significantly reduces the number of parameters to less than 50k and floating-point operations (FLOPs) to less than 3.02M, thereby fully showcasing the immense potential of frequency analysis and the Mamba structure in HSIC.