Pseudo-label　learning-based　methods　usually　regard　class　confidence　above　a　certain　threshold　for　unlabeled　samples　as　pseudo-labels,　which　may　result　in　pseudo-labels　still　containing　wrong　labels.　In　this　letter,　we　propose　a　prototype-based　pseudo-label　refinement　(PPLR)　for　semi-supervised　hyperspectral　image　classification.　The　proposed　PPLR　filters　wrong　labels　from　pseudo-labels　using　class　prototypes,　which　can　improve　the　discrimination　of　the　network.　First,　PPLR　uses　multi-head　attentions　to　extract　the　spectral-spatial　features,　and　designs　an　adaptive　threshold　that　can　be　dynamically　adjusted　to　generate　high-confidence　pseudo-labels.　Then,　PPLR　constructs　class　prototypes　for　different　categories　using　labeled　sample　features　and　unlabeled　sample　features　with　refined　pseudo-labels　to　improve　the　quality　of　pseudo-labels　by　filtering　wrong　labels.　Finally,　PPLR　further　assigns　reliable　weights　to　these　pseudo-labels　in　calculating　their　supervised　loss,　and　introduces　a　center　loss　to　improve　the　discrimination　of　features.　When　10　labeled　samples　per　category　are　utilized　for　training,　PPLR　achieves　the　overall　accuracies　of　82.11%,　86.70%　and　92.50%　on　the　Indian　Pines,　Houston2013　and　Salinas　datasets,　respectively.　IEEE