A Cooperative Meta-Learning and Spectral Diversity Adaptation Framework for Hyperspectral Target Detection

Meta-learning has demonstrated significant potential in addressing the limited annotation data challenge in hyperspectral target detection. However, existing meta-learningbased methods face two major challenges: 1) weak inter-task correlation leading to unstable optimization directions, and 2) meta-knowledge adaptation based on single prior spectral information fails to effectively characterize spectral variation properties of targets in new scenarios. To overcome these challenges, this letter proposes a cooperative meta-learning framework with spectral diversity adaptation for hyperspectral target detection. The framework introduces a co-learner through cooperative learning to dynamically capture cross-task knowledge and stabilize optimization directions, while designing a spectral diversity-based meta-knowledge adaptation strategy to enhance the model’s ability to understand the spectral variation characteristics of targets in new scenarios and precisely distinguish the spectral features between targets and backgrounds. Experimental results on two public datasets demonstrate that the proposed method outperforms state-of-the-art hyperspectral target detection algorithms. The code is available at https://github.com/Li-ZK/CMLSDA.