Roads　are　important　mode　of　transportation,　which　are　very　convenient　for　people＇s　daily　work　and　life.　However,　it　is　challenging　to　accuratly　extract　road　information　from　a　high-resolution　remote　sensing　image.　This　paper　presents　a　road　extraction　method　for　remote　sensing　images　with　a　complement　UNet　(C-UNet).　C-UNet　contains　four　modules.　Firstly,　the　standard　UNet　is　used　to　roughly　extract　road　information　from　remote　sensing　images,　getting　the　first　segmentation　result;　secondly,　a　fixed　threshold　is　utilized　to　erase　partial　extracted　information;　thirdly,　a　multi-scale　dense　dilated　convolution　UNet　(MD-UNet)　is　introduced　to　discover　the　complement　road　areas　in　the　erased　masks,　obtaining　the　second　segmentation　result;　and,　finally,　we　fuse　the　extraction　results　of　the　first　and　the　third　modules,　getting　the　final　segmentation　results.　Experimental　results　on　the　Massachusetts　Road　dataset　indicate　that　our　C-UNet　gets　the　higher　results　than　the　state-of-the-art　methods,　demonstrating　its　effectiveness.