With　the　rapid　development　of　remote-sensing　earth　observation　technology,　hyperspectral　imagery　has　shown　exponential　growth.　The　quick　and　accurate　retrieval　of　hyperspectral　images　has　become　a　practical　challenge　in　applications.　Moreover,　open　network　sharing　has　rendered　network　information　security　increasingly　important.　It　is　necessary　to　prevent　breach　of　confidentiality　events　during　retrieval,　particularly　for　hyperspectral　images　containing　crucial　information.　Therefore,　a　method　for　hyperspectral　image　secure　retrieval　based　on　encrypted　deep　spectral-spatial　features　is　proposed.　In　principle,　our　method　includes　the　following　steps:　(1)　Considering　the　powerful　feature　learning　capability　of　deep　networks,　deep　spectral-spatial　features　of　hyperspectral　image　are　extracted　with　a　deep　convolutional　generative　adversarial　network.　(2)　For　high-dimensional　deep　features,　t-distributed　Stochastic　neighbor　embedding　based　nonlinear　manifold　hashing　is　utilized　to　reduce　the　dimensionality　of　deep　spectral-spatial　features.　(3)　To　ensure　data　security　during　retrieval,　deep　spectral-spatial　features　are　encrypted　with　feature　randomization　encryption.　(4)　Multi-index　hashing　is　utilized　to　measure　similarities　among　the　deep　spatial-spectral　features　of　hyperspectral　images.　(5)　Relevance　feedback　based　on　feature　reweighting　is　introduced　to　further　improve　retrieval　accuracy.　Four　experiments　are　conducted　to　prove　the　effectiveness　of　the　proposed　method　based　on　retrieval　and　security　performance.　Our　experimental　results　on　two　hyperspectral　datasets　show　that　our　method　can　effectively　protect　the　security　of　image　content　with　sufficient　image　retrieval　accuracy.　(C)　2019　Society　of　Photo-Optical　Instrumentation　Engineers　(SPIE)