Hyperspectral Target Detection Based on Unsupervised Contrastive Learning and Spatial–Spectral Knowledge Distillation

Hyperspectral target detection (HTD) faces challenges due to limited labeled data and high spectral similarity across material samples, making feature extraction difficult. Existing methods for spatial-spectral feature learning are often computationally constrained. To address these issues, we propose a framework combining unsupervised spectral contrastive learning and spatial-spectral knowledge distillation (UCLKD). Our approach uses an unsupervised contrastive learning (UCL) module to enhance spectral feature representation while preventing model collapse. A spatial-spectral knowledge distillation (KD) strategy then leverages features from a hyperspectral foundation model (FM) to guide the spectral feature extraction network, effectively capturing discriminative spectral features. Extensive experiments demonstrate that UCLKD outperforms existing methods in target detection performance. The code is available at https://github.com/Li-ZK/UCLKD-2025