Most　remote　sensing　scene　classification　methods　are　primarily　built　upon　the　closed-set　assumption,　which　assumes　that　all　test　samples　definitely　belong　to　one　of　the　categories　seen　during　training.　However,　practical　applications　are　usually　open　environments,　where　samples　of　other　categories　that　have　never　been　seen　during　training　will　appear,　which　is　called　open-set　remote　sensing　scene　classification　(OS-RSSC).　These　methods　may　mistakenly　classify　samples　of　unseen　categories　into　those　seen　categories,　resulting　in　a　decrease　in　application　potential.　In　this　paper,　we　propose　a　positive-negative　prompt　learning　(PNPL)　framework　for　OS-RSSC.　PNPL　aims　to　tune　the　powerful　contrastive　language-image　pre-training　(CLIP)　model　for　OS-RSSC　through　learning　positive　and　negative　prompts.　First,　positive　textual　prompts　and　visual　prompts　are　trained　to　provide　the　model　with　basic　classification　capabilities　for　known　classes.　Then,　negative　textual　prompts　are　indirectly　learned　from　positive　prompts　and　images,　enabling　the　model　to　capture　the　semantics　of　unknown　classes.　PNPL　significantly　increases　the　discriminative　power　between　known　and　unknown　classes,　enhancing　the　model’s　ability　to　accurately　distinguish　them.　Extensive　experiments　on　three　RSSI　datasets　have　shown　that　PNPL　outperforms　compared　methods.　©　1980-2012　IEEE.