Language-Guided and Similarity-Aware Network for Few-Shot Classification of Coastal Wetland Hyperspectral Images

In recent years, few-shot learning (FSL) has made significant progress in hyperspectral image classification (HSIC) by transferring meta-knowledge from a source domain with sufficient labeled samples to a target domain with limited labeled samples. However, existing FSL methods face two key challenges in coastal wetland HSIC applications, i.e., prototype instability due to limited labeled samples and domain shift due to the domain’s distribution difference. These challenges are further exacerbated by the unique characteristics of coastal wetland environments, which have complex land cover classes and subtle spectral differences between land cover classes. To address these limitations, we propose a language-guided and similarity-aware network (LGSAnet) for few-shot coastal wetland HSIC in this article. The network mainly consists of two modules: language-guided prototype alignment (LGPA) and similarity-aware prototype calibration (SAPC). The LGPA module uses linguistic features to guide the learning of visual features, enabling the model to obtain visual representations enriched with linguistic prior knowledge. This guided alignment can learn more accurate and stable visual prototype representations, thereby improving the accuracy of the model. To mitigate domain shift, an SAPC module is constructed to refine the target domain prototypes and minimize domain-specific differences using the domain’s semantic similarity. The experimental results on three coastal wetland hyperspectral datasets demonstrate that the proposed LGSAnet outperforms existing state-of-the-art FSL methods.