Content-Based　Image　Retrieval　(CBIR)　for　common　images　has　been　thoroughly　explored　in　recent　years,　but　little　attention　has　been　paid　to　hyperspectral　remote　sensing　images.　How　to　extract　appropriate　hyperspectral　remote　sensing　image　feature　is　a　fundamental　task　for　retrieving　large-scale　similar　images.　At　present,　endmember　as　hyperspectral　image　feature　has　presented　more　spectral　descriptive　ability.　Visual　words　feature　is　a　feasible　method　to　describe　image　content,　which　can　achieve　scalability　for　large-scale　image　retrieval.　In　this　article,　spectral　words　are　created　for　hyperspectral　remote　sensing　image　retrieval　by　combining　both　spatial　and　spectral　information.　Firstly,　spatial　and　spectral　features　are　extracted　respectively　using　spectral　saliency　model　and　endmember　extraction.　Then　a　spectral　vocabulary　tree　is　constructed　by　feature　clustering,　in　which　the　cluster　centers　are　considered　as　the　spectral　words.　Finally,　the　spectral　words　are　compared　for　finding　the　similar　hyperspectral　remote　sensing　images.　Experimental　results　on　NASA　datasets　show　that　the　spectral　words　can　improve　the　accuracy　of　hyperspectral　image　retrieval,　which　further　prove　our　method　has　more　descriptive　ability.　©　Springer　International　Publishing　AG　2016.