A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification

arXiv:2404.01673v2 Announce Type: replace
Abstract: Hyperspectral image (HSI) classification techniques have been intensively studied and a variety of models have been developed. However, these HSI classification models are confined to pocket models and unrealistic ways of datasets partitioning. The former limits the generalization performance of the model and the latter is partitioned leads to inflated model evaluation metrics, which results in plummeting model performance in the real world. Therefore, we propose a universal knowledge embedded contrastive learning framework (KnowCL) for …

arxiv classification cs.cv embedded framework image knowledge type universal