With　the　rapid　development　of　remote　sensing　technology,　searching　the　similar　image　is　a　challenge　for　hyperspectral　remote　sensing　image　processing.　Meanwhile,　the　dramatic　growth　in　the　amount　of　hyperspectral　remote　sensing　data　has　stimulated　considerable　research　on　content-based　image　retrieval　(CBIR)　in　the　field　of　remote　sensing　technology.　Although　many　CBIR　systems　have　been　developed,　few　studies　focused　on　the　hyperspectral　remote　sensing　images.　A　CBIR　system　for　hyperspectral　remote　sensing　image　using　endmember　extraction　is　proposed　in　this　paper.　The　main　contributions　of　our　method　are　that:　(1)　the　endmembers　as　the　spectral　features　are　extracted　from　hyperspectral　remote　sensing　image　by　improved　automatic　pixel　purity　index　(APPI)　algorithm;　(2)　the　spectral　information　divergence　and　spectral　angle　match　(SID-SAM)　mixed　measure　method　is　utilized　as　a　similarity　measurement　between　hyperspectral　remote　sensing　images.　At　last,　the　images　are　ranked　with　descending　and　the　top-M　retrieved　images　are　returned.　The　experimental　results　on　NASA　datasets　show　that　our　system　can　yield　a　superior　performance.