Global Feature and Semantic Information Extraction Network Based on Frozen SAM Encoder for Hyperspectral Image Classification

Nowadays, various types of foundational models have emerged, showcasing remarkable performance across a multitude of downstream tasks. However, in the domain of hyperspectral image classification (HSIC), substantial research is still required to effectively leverage the advantages of foundational models and adapt them to hyperspectral data. Consequently, we propose a HSIC algorithm based on a fixed-parameter SAM encoder. Specifically, the global feature extraction subnetwork integrates global patch information to obtain processed features. Subsequently, the semantic information extraction subnetwork is trained using cross-entropy to extract semantic features of categories, culminating in pixel-level classification. Experiments on two HSI datasets indicate that the proposed method can obtain better classification performance when compared with seven state-of-the-art methods.