With　the　rapid　growth　of　the　capabilities　for　hyperspectral　imagery　acquisition,　how　to　efficiently　find　the　significant　target　in　hyperspectral　imagery　has　become　a　fundamental　task　for　remote-sensing　applications.　Existing　target　extraction　methods　mainly　separate　targets　from　background　with　a　threshold　based　on　pixels　and　single-scale　image　information　extraction.　However,　due　to　the　high　dimensional　characteristics　and　the　complex　background　of　hyperspectral　imagery,　it　is　difficult　to　obtain　good　extraction　results　with　existing　methods.　Saliency　detection　has　been　a　promising　topic　because　saliency　features　can　quickly　locate　saliency　regions　from　complex　backgrounds.　Considering　the　spatial　and　spectral　characteristics　of　a　hyperspectral　image,　a　multiscale　target　extraction　method　using　a　spectral　saliency　map　is　proposed　for　a　hyperspectral　image,　which　includes:　(1)　a　spectral　saliency　model　is　constructed　for　detecting　spectral　saliency　map　in　a　hyperspectral　image;　(2)　focus　of　attention　(FOA)　as　the　seed　point　is　competed　in　the　spectral　saliency　map　by　the　winner-take-all　(WTA)　network;　(3)　the　multiscale　image　is　segmented　by　region　growing　based　on　the　minimum-heterogeneity　rule　after　calculating　the　heterogeneity　of　the　seed　point　with　its　surrounding　pixels;　(4)　the　salient　target　is　detected　and　segmented　under　the　constraint　of　the　spectral　saliency　map.　The　experimental　results　show　that　the　proposed　method　can　effectively　improve　the　accuracy　of　target　extraction　for　hyperspectral　images.　(C)　2016　Optical　Society　of　America