DNMFNet: Unsupervised deep feature extraction for hyperspectral image classification with limited labels

• Deep Unsupervised Convolutional Kernels: A deep hierarchical NMF architecture is proposed to learn multi-layer convolution kernels in an unsupervised manner. • Unsupervised Hybrid Spectral-Spatial Modules: Novel synergistic integration of PCA, Whitening, NMF, Convolution, ReLU, and texture-aware LBP features. • Progressive Feature Abstraction: Multi-layer NMF framework enables progressively abstract, discriminative feature learning from unlabeled HSI data. • Breaks Shallow NMF Barrier: First deep NMF extension overcoming traditional limitations, capturing complex HSI structures efficiently. • Maintains Efficiency & Interpretability: Combines CNN-like hierarchical feature learning with NMF’s computational efficiency and inherent interpretability. Hyperspectral image (HSI) classification is a key task in remote sensing but significantly limited by the scarcity of labeled training samples. Traditional Nonnegative Matrix Factorization (NMF) provides interpretable representation but lacks the depth to extract discriminative features for complex HSI data. Deep learning methods, while powerful, require abundant labels that are often unavailable. To address this, we propose DNMFNet that a deep NMF network for unsupervised feature extraction. Its core innovation lies in learning hierarchical convolutional kernels via NMF without supervision, enabling deep representation learning under extreme label scarcity. Specifically, we extend NMF into a multi-layer architecture that integrates PCA, whitening, NMF convolution, and ReLU operations, forming modules capable of extracting rich spectral-spatial features. A classification framework built on these features is then developed. Experiments on three benchmark HSI datasets demonstrate that DNMFNet consistently outperforms state-of-the-art methods designed for few-shot learning.