WMM: noise-robust hyperspectral image classification via joint wavelet-manifold mamba framework

Hyperspectral Images (HSIs) classification plays a vital role in precision agriculture and environmental monitoring. However, real-world HSIs is frequently degraded by complex noise, which compromises both spectral integrity and spatial consistency. Existing deep learning (DL) methods, such as Convolutional Neural Networks (CNNs) and Transformers, often face trade-offs between noise suppression and feature preservation, or suffer from high computational costs. To address these challenges, this paper proposes a noise-robust framework named Wavelet-Manifold-Mamba (WMM). Unlike straightforward modular concatenation, WMM establishes a deep coupling between physical-level signal restoration and geometric-level manifold alignment. Specifically, recognizing that the recursive state propagation in Mamba is highly sensitive to input perturbations, a Multi-scale Wavelet Denoising Block (MWDB) is first employed to suppress high-frequency artifacts via learnable cross-frequency attention, thereby stabilizing the token sequence for subsequent modeling. Complementarily, to rectify the feature drift that persists even after denoising, a Manifold-Regularized Mamba Block (MRMB) leverages a topological loss to enforce noise-invariant manifold embeddings. Furthermore, the architecture incorporates a selective scanning mechanism to capture long-range dependencies with linear complexity. Experiments on the WHU-Hi and SZU-Tree datasets demonstrate that the proposed WMM outperforms state-of-the-art methods in noise-robust classification accuracy.