The　researches　on　saliency　detection　for　common　images　have　been　thoroughly　explored　in　recent　years,　but　little　attention　has　been　paid　to　hyperspectral　images.　However,　salient　region　detection　can　contribute　to　finding　significant　targets　in　hyperspectral　images.　In　this　paper,　spectral　saliency　is　introduced　to　the　spectral　domain　for　detecting　targets　in　hyperspectral　images,　which　are　computed　by　expanding　the　Itti＇s　visual　attention　model.　Firstly,　spatial　and　spectral　features　are　extracted　from　hyperspectral　image.　The　conspicuity　feature　of　the　first　three　principal　components,　orientations,　spectral　angle　and　visible　spectral　band　opponent　are　computed　to　sum　into　a　saliency　map.　Finally,　salient　targets　are　detected　by　winner-take-all　strategy　finding　the　region　with　the　highest　value　in　the　saliency　map.　Experimental　results　show　that　the　proposed　method　have　better　effectiveness　of　target　detection　compared　with　the　method　based　on　RGB　image　and　visible　spectrum.