SS³L: Self-Supervised Spectral–Spatial Subspace Learning for Hyperspectral Image Denoising

Highlights: What are the main findings? A dual-domain self-supervised hyperspectral denoising framework that disentangles noise and signal from single noisy inputs without requiring paired training data was developed. SS³L integrates adaptive rank subspace representation with a noise-aware spectral–spatial hybrid loss constrained self-supervised learning framework, achieving robust denoising across varying noise levels and different scenes. What is the implication of the main finding? The proposed framework eliminates the requirement for clean training data and manual hyperparameter tuning, addressing key limitations of existing denoising methods in hyperspectral restoration research. By enhancing structural fidelity and spectral accuracy under complex noise conditions, SS³L improves the quality and usability of hyperspectral data for remote sensing applications such as classification, detection, and monitoring across different sensors. Hyperspectral imaging (HSI) systems often suffer from complex noise degradation during the imaging process, significantly impacting downstream applications. Deep learning-based methods, though effective, rely on impractical paired training data, while traditional model-based methods require manually tuned hyperparameters and lack generalization. To address these issues, we propose SS³L (Self-Supervised Spectral-Spatial Subspace Learning), a novel HSI denoising framework that requires neither paired data nor manual tuning. Specifically, we introduce a self-supervised spectral–spatial paradigm that learns noisy features from noisy data, rather than paired training data, based on spatial geometric symmetry and spectral local consistency constraints. To avoid manual hyperparameter tuning, we propose an adaptive rank subspace representation and a loss function designed based on the collaborative integration of spectral and spatial losses via noise-aware spectral-spatial weighting, guided by the estimated noise intensity. These components jointly enable a dynamic trade-off between detail preservation and noise reduction under varying noise levels. The proposed SS³L embeds noise-adaptive subspace representations into the dynamic spectral–spatial hybrid loss-constrained network, enabling cross-sensor denoising through prior-informed self-supervision. Experimental results demonstrate that SS³L effectively removes noise while preserving both structural fidelity and spectral accuracy under diverse noise conditions.