SSFSL: Self-Supervised and Few-Shot Learning for Cross-Domain Hyperspectral Image Classification

Abstract

Few-shot learning (FSL) has gained increasing attention in hyperspectral image (HSI) classification due to its ability to perform cross-domain classification with minimal labeled samples. However, existing FSL methods overlook the continuity of HSI spectral sequences and fail to utilize the large amount of unlabeled samples in the target domain. To address these issues, we introduce a novel cross-domain HSI classification method that combines self-supervised learning with FSL (SSFSL). This approach uses self-supervised learning and FSL to extract transferable knowledge from the source domain and introduces an adaptive soft label generation algorithm to leverage unlabeled samples in the target domain. Compared to existing cross-domain FSL classification methods, the proposed approach considers the spectral sequence continuity of HSI and effectively extracts useful information from unlabeled samples in the target domain. Extensive experiments conducted on three datasets demonstrate that SSFSL outperforms state-of-the-art methods in both quantitative and qualitative aspects.