Hyperspectral　images　are　one　of　the　most　important　fundamental　and　strategic　information　resources,　imaging　the　same　ground　object　with　hundreds　of　spectral　bands　varying　from　the　ultraviolet　to　the　microwave.　With　the　emergence　of　huge　volumes　of　high-resolution　hyperspectral　images　produced　by　all　sorts　of　imaging　sensors,　processing　and　analysis　of　these　images　requires　effective　retrieval　techniques.　How　to　ensure　retrieval　accuracy　and　efficiency　is　a　challenging　task　in　the　field　of　hyperspectral　image　retrieval.　In　this　paper,　an　efficient　hyperspectral　image　retrieval　method　is　proposed.　In　principle,　our　method　includes　the　following　steps:　(1)　in　order　to　make　powerful　representations　for　hyperspectral　images,　deep　spectral-spatial　features　are　extracted　with　the　Deep　Convolutional　Generative　Adversarial　Networks　(DCGAN)　model;　(2)　considering　the　higher　dimensionality　of　deep　spectral-spatial　features,　t-Distributed　Stochastic　Neighbor　Embedding-based　Nonlinear　Manifold　(t-SNE-based　NM)　hashing　is　utilized　to　make　dimensionality　reduction　by　learning　compact　binary　codes　embedded　on　the　intrinsic　manifolds　of　deep　spectral-spatial　features　for　balancing　between　learning　efficiency　and　retrieval　accuracy;　and　(3)　multi-index　hashing　in　Hamming　space　is　measured　to　find　similar　hyperspectral　images.　Five　comparative　experiments　are　conducted　to　verify　the　effectiveness　of　deep　spectral-spatial　features,　dimensionality　reduction　of　t-SNE-based　NM　hashing,　and　similarity　measurement　of　multi-index　hashing.　The　experimental　results　using　NASA　datasets　show　that　our　hyperspectral　image　retrieval　method　can　achieve　comparable　and　superior　performance　with　less　computational　time.