Few-Shot Learning for Hyperspectral Imaging via Spectral-Spatial Feature Reconstruction

In recent years, to reduce the workload on operators in annotating hyperspectral images (HSI), researchers have started to employ few-shot learning (FSL) methods for HSI classification. During this process, it is typically necessary to combine spatial and spectral features of HSI into a single vector representation, a fusion that often compromises the detail of spatial features. To optimize the model’s performance in FSL using spectral and channel information, this study has designed an FSL network based on the reconstruction of spectral-spatial features (S-SRFSL). The network comprises two main stages: In the first stage, we introduce an innovative loss function to prevent channel aggregation phenomena during HSI channel selection; in the second stage, we utilize the HSI results obtained from the first stage to extract spatial, spectral, and joint spectral-spatial features. These three types of feature vectors are then combined into a feature matrix for reconstruction, facilitating classification tasks in a new feature space. The study selected four publicly available HSI datasets for model pre-training and two for testing. Experimental results demonstrate that S-SRFSL proposed in this study achieves superior performance on both test datasets.