The　hyperspectral　images　are　represented　as　an　image　cube,　which　image　the　same　ground　object　with　several　tens　to　hundreds　of　spectral　bands　from　ultraviolet　to　the　microwave　range.　Content-Based　Image　Retrieval　(CBIR)　for　hyperspectral　images　has　been　explored　in　recent　years.　However,　it　is　still　a　concern　and　challenging　task　to　extract　a　highly　descriptive　feature　to　contribute　to　improve　the　performance　of　hyperspectral　image　retrieval.　At　present,　deep　learning　is　a　new　research　area　of　machine　learning,　which　can　extract　more　effective　deep　features　by　using　a　cascade　of　many　layers　of　nonlinear　processing　units.　In　this　paper,　a　deep　spectral-spatial　feature　extraction　method　is　proposed　based　on　Deep　Convolutional　Generative　Adversarial　Networks　(DCGAN)　for　hyperspectral　image　retrieval.　Firstly,　spectral　vector　is　extracted　by　selecting　manually　pure　pixels　from　hyperspectral　image.　Then　spatial　vector　is　extracted　by　selecting　manually　the　neighbor　pixels　of　pure　pixels　from　principal　bands　after　reducing　the　dimensionality　of　hyperspectral　images　with　one-bit　(1BT)　transform.　Spectral-spatial　vector　as　training　samples　is　obtained　by　combining　spectral　vector　with　spatial　vector　by　using　Vector　Stacking　(VS)　approach.　After　training　DCGAN　model,　deep　spectral-spatial　feature　is　extracted　to　further　apply　into　hyperspectral　retrieval.　Experiments　are　conducted　among　our　method　and　the　three　other　state-of-the-art　methods　including　endmember　extraction　using　improved　Automatic　Pixel　Purity　Index　(APPI),　spectral　and　spatial　features　extraction,　and　endmember　signatures　extraction　using　Endmember　Induction　Algorithm　(EIA).　Experimental　results　on　AVIRIS　data　show　that　our　method　can　achieve　a　higher　accuracy　for　hyperspectral　image　retrieval　and　further　prove　our　extracted　deep　spectral-spatial　feature　has　stronger　descriptive　ability.　©　2017　IEEE.