Self-Supervised Learning Driven Cross-Domain Feature Fusion Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification faces significant challenges due to the high cost of acquiring labeled samples.To mitigate this, we propose SSCF-Net, a novel self-supervised learning driven cross-domain feature fusion Network.SSCF-Net uniquely leverages readily available labeled natural images (source domain) to aid HSI (target domain) classification by transfer learning.Specifically, we employ rotation-based self-supervision in the source domain to learn transferable features, which are then transferred to the HSI domain.Within SSCF-Net, we effectively fuse local and global features: local features are extracted by a jointly trained module combining VGG and two-dimensional long short-term memory networks (TD-LSTM) networks, while global features capturing long-range dependencies are learned via a Transformer model.Crucially, in the HSI domain, we further employ contrastive learning as a self-supervised strategy to maximally utilize the limited labeled data.Extensive experiments on three benchmark HSI datasets (Salinas, Indian Pines, WHU-Hi-LongKou) demonstrate that SSCF-Net significantly outperforms existing methods, validating its effectiveness in addressing the label scarcity problem.