Geographical Dual-Prior Guided Few-Shot Network for Cross-Domain Hyperspectral Image Classification

Cross-domain hyperspectral image (HSI) classification (HSIC) addresses the challenge of real-time labeling of new regions. To mitigate the performance decline caused by unseen classes, a few-shot learning (FSL) method is used. However, these methods fail to fully consider the problem of sample scarcity and classification imbalance due to FSL methods. In addition, the issue of category confusion stemming from localized spectral fluctuations within the same class is commonly overlooked. To solve these problems, a geographical dual-prior guided few-shot network (Gprior-FSN) is proposed. In Gprior-FSN, combining prior knowledge of the first law of geography, a geographical prior guided bicorrelated (G-B) sample enhancement mechanism is proposed which includes geospatially correlated enhancement (GCE) and spectral feature correlated enhancement (SFCE). GCE uses a hierarchical sampling strategy to tackle the inherent imbalance problem for FSL methods. Subsequently, GCE mitigates sample scarcity via neighborhood sample expansion while identifying candidate pseudosamples with geospatial correlation. To make the acquired pseudosamples of the same category bicorrelated in both geospatial and spectral features, G-B combining spectral feature clustering and probabilistic statistics mechanism is designed. Inspired by the second law of geography, Gprior-FSN uses a spatial constraint mechanism to effectively enhance intraclass similarity by reducing spatial local heterogeneity, while improving global interclass discriminability. Finally, to further capture representative spatial-spectral feature, a weighted dual feature fusion network is designed. Experimental results from three distinct HSI datasets show that Gprior-FSN outperforms advanced HSIC methods in both efficiency and accuracy. In addition, the Gprior-FSN demonstrates strong generalization performance on real GF-5 image.