Roads　as　important　artificial　objects　are　the　main　body　of　modern　traffic　system,　which　provide　many　conveniences　for　human　civilization.　With　the　development　of　remote　sensing　and　hyperspectral　imaging　technology,　how　to　automatically　and　accurately　extract　road　network　from　high-resolution　multispectral　satellite　images　has　become　a　hot　and　challenging　research　topic　of　geographic　information　technology.　In　this　paper,　an　automatic　road　extraction　method　from　high-resolution　multispectral　satellite　images　is　proposed　by　using　multiple　saliency　features.　Firstly,　road　edge　is　extracted　by　detecting　local　linear　edge　with　Singular　value　decomposition　(SVD).　Secondly,　road　regions　are　constructed　by　K-means　clustering　after　extracting　the　feature　of　background　difference.　Then　road　network　is　achieved　by　integrating　multiple　saliency　features　with　Total　variation　(TV)　based　image　fusion　algorithm.　Finally,　the　non-road　parts　and　noises　are　removed　from　road　network　by　optimizing　multiple　salient　features　with　post-processing　and　morphological　operations.　The　experimental　results　show　that　the　proposed　method　can　achieve　a　superior　performance　in　completeness　and　correctness.