WS-Net: Weak-Signal Representation Learning and Gated Abundance Reconstruction for Hyperspectral Unmixing via State-Space and Weak Signal Attention Fusion

TL;DR

WS-Net is introduced, a deep unmixing framework specifically designed to address weak-signal collapse through state-space modelling and Weak Signal Attention fusion, establishing WS-Net as a robust and computationally efficient benchmark for weak-signal hyperspectral unmixing.

Abstract

Weak spectral responses in hyperspectral images are often obscured by dominant endmembers and sensor noise, resulting in inaccurate abundance estimation. This paper introduces WS-Net, a deep unmixing framework specifically designed to address weak-signal collapse through state-space modelling and Weak Signal Attention fusion. The network features a multi-resolution wavelet-fused encoder that captures both high-frequency discontinuities and smooth spectral variations with a hybrid backbone that integrates a Mamba state-space branch for efficient long-range dependency modelling. It also incorporates a Weak Signal Attention branch that selectively enhances low-similarity spectral cues. A learnable gating mechanism adaptively fuses both representations, while the decoder leverages KL-divergence-based regularisation to enforce separability between dominant and weak endmembers. Experiments on one simulated and two real datasets (synthetic dataset, Samson, and Apex) demonstrate consistent improvements over six state-of-the-art baselines, achieving up to 55% and 63% reductions in RMSE and SAD, respectively. The framework maintains stable accuracy under low-SNR conditions, particularly for weak endmembers, establishing WS-Net as a robust and computationally efficient benchmark for weak-signal hyperspectral unmixing.

Figures (10)

• Figure 1: Overview of the proposed WS-Net framework. (1) WFFE: A multi-resolution wavelet-fused module that captures spatial–spectral cues across scales and enhances low-reflectance signals via Haar and Symlet-3 convolutions. (2) Mamba SSM and Inverse Attention Fusion: A dual-branch backbone integrating Mamba state-space modelling for efficient long-range spectral propagation with inverse attention for selective weak-signal enhancement. (3) Sparsity-Aware Decoder: Reconstructs hyperspectral data from softmax-constrained abundance maps while enforcing sparsity and distributional separation through KL-divergence regularisation.
• Figure 2: Architecture of the proposed Mamba SSM and Inverse Attention Fusion module. The framework integrates a Mamba-based state-space branch for long-range spectral modelling with a weak-signal Weak Signal Attention branch that selectively amplifies low-energy features suppressed by noise. The Transformer pathway introduces a class token with patch-level attention to provide global contextual guidance for hyperspectral unmixing.
• Figure 3: Dataset visualizations: (a) Simulated dataset, (b) Samson dataset, (c) Apex dataset. For each dataset, we show (left) a pseudo-RGB image (R: 650 nm, G: 532 nm, B: 450 nm), (middle) representative endmember abundance maps (normalized; brighter indicates higher abundance), and (right) endmember spectral signatures (reflectance versus wavelength).
• Figure 4: Abundance maps generated using the jet colourmap for the synthetic dataset. Each small image represents the spatial distribution of a specific endmember.
• Figure 5: Visual comparison synthetic dataset of endmember spectra obtained by different unmixing techniques. The blue curves correspond to the ground‐truth spectra, while the orange curves denote the spectra estimated by each method.