Attentive Cross-Domain Few-shot Learning and Domain Adaptation in HSI Classification

This study explores the application of Attentive Cross-Domain Few-Shot Learning (ACDFSL) in Hyperspectral Image (HSI) Classification, specifically addressing challenges associated with environments possessing limited labeled data. Our approach applies the Squeeze-and-Excitation (SE) attention and Residual elements within a deep learning architecture of four convolution blocks. This innovative strategy of integrating attention mechanisms into few-shot learning models represents a significant departure from traditional practices. After rigorous assessment, the ACDFSL model showcased outstanding results, revealing performance rates of 92.14%, 96.23%, and 91.27% in OA, AA, and Kappa, respectively, on the Salinas dataset. Additionally, the model attained rates of 85.67%, 89.66%, and 85.4% on the University of Pavia (PU) dataset. These results indicate an edge over existing state-of-the-art techniques such as SVM, 3D-CNN, SSRN, and other DFSL variants. This considerable progress emphasizes the potential and applicability of the ACDFSL approach in real-world HSI Classification scenarios, especially where labeled data is sparse, and paves the way for future research in this sphere.