Although　many　content-based　image　retrieval　systems　have　been　developed,　few　studies　have　focused　on　hyperspectral　remote　sensing　images.　In　this　paper,　a　hyperspectral　remote　sensing　image　retrieval　system　based　on　spectral　and　texture　features　is　proposed.　The　main　contributions　are　fourfold:　(1)　considering　the　＂mixed　pixel＂　in　the　hyperspectral　image,　endmembers　as　spectral　features　are　extracted　by　an　improved　automatic　pixel　purity　index　algorithm,　then　the　texture　features　are　extracted　with　the　gray　level　co-occurrence　matrix;　(2)　similarity　measurement　is　designed　for　the　hyperspectral　remote　sensing　image　retrieval　system,　in　which　the　similarity　of　spectral　features　is　measured　with　the　spectral　information　divergence　and　spectral　angle　match　mixed　measurement　and　in　which　the　similarity　of　textural　features　is　measured　with　Euclidean　distance;　(3)　considering　the　limited　ability　of　the　human　visual　system,　the　retrieval　results　are　returned　after　synthesizing　true　color　images　based　on　the　hyperspectral　image　characteristics;　(4)　the　retrieval　results　are　optimized　by　adjusting　the　feature　weights　of　similarity　measurements　according　to　the　user＇s　relevance　feedback.　The　experimental　results　on　NASA　data　sets　can　show　that　our　system　can　achieve　comparable　superior　retrieval　performance　to　existing　hyperspectral　analysis　schemes.　(C)　2017　Optical　Society　of　America