Deep　learning　(DL)　has　shown　great　potential　for　hyperspectral　image　(HSI)　classification.　However,　DL　models　easily　get　trapped　into　overfitting　due　to　limited　training　samples.　To　overcome　this　issue,　a　novel　spectral-spatial　triplet　network　(S2TNet)　is　proposed　for　few-shot　HSI　classification.　First,　a　lightweight　spectral-spatial　network　(SSN)　composed　of　1-D　and　2-D　convolution　is　introduced　to　extract　spectral-spatial　features.　Second,　a　hard　sample　selection　strategy　is　proposed　by　integrating　classification　and　contrast　training　to　deal　with　unbalanced　positive　and　negative　samples　in　traditional　triplet　networks.　Third,　an　enhanced　triplet　loss　function　is　proposed　by　considering　the　relationship　between　positive　and　negative　sample　pairs　to　ensure　the　distance　between　homogeneous　samples　is　smaller　than　that　of　heterogeneous　samples,　which　effectively　improves　the　discrimination　ability　of　the　model.　Experiments　conducted　on　two　widely　used　hyperspectral　datasets　demonstrate　that　S2TNet　significantly　outperforms　other　related　methods,　with　0.81%-16.83%　and　1.40%-13.83%　improvements　(under　20　labeled　samples　per　class　for　training)　in　terms　of　overall　accuracy　(OA)　in　Indian　Pine　(IP)　data　and　University　of　Pavia　(PU),　respectively.　　©　2004-2012　IEEE.