While　salient　object　detection　has　been　studied　intensively　by　the　computer　vision　and　pattern　recognition　community,　there　are　still　great　challenges　in　practical　applications,　especially　when　perceived　objects　have　similar　appearance　such　as　intensity,　color,　and　orientation,　but　different　materials.　Traditional　methods　do　not　provide　good　solution　to　this　problem　since　they　were　mostly　developed　on　color　images　and　do　not　have　the　full　capability　in　discriminating　materials.　More　advanced　technology　and　methodology　are　in　demand　to　gain　access　to　further　information　beyond　human　vision.　In　this　paper,　we　extend　the　concept　of　salient　object　detection　to　material　level　based　on　hyperspectral　imaging　and　present　a　material-based　salient　object　detection　method　which　can　effectively　distinguish　objects　with　similar　perceived　color　but　different　spectral　responses.　The　proposed　method　first　estimates　the　spatial　distribution　of　different　materials　or　endmembers　using　a　hyperspectral　unmixing　approach.　This　step　enables　the　calculation　of　a　conspicuity　map　based　on　the　global　spatial　variance　of　spectral　responses.　Then　the　multi-scale　center-surround　difference　of　local　spectral　features　is　calculated　via　spectral　distance　measures　to　generate　local　spectral　conspicuity　maps.　These　two　types　of　conspicuity　maps　are　fused　for　the　final　salient　object　detection.　A　new　dataset　of　45　hyperspectral　images　is　introduced　for　experimental　validation.　The　results　show　that　our　method　outperforms　several　existing　hyperspectral　salient　object　detection　approaches　and　the　state-of-the-art　methods　proposed　for　RGB　images.　(C)　2017　Elsevier　Ltd.　All　rights　reserved.