Roads,　as　important　artificial　objects,　are　the　main　body　of　modern　traffic　system,　providing　many　conveniences　for　human　civilization.　With　the　development　of　Intelligent　Transportation　Systems　(ITS),　the　road　structure　is　changing　frequently.　Road　recognition　is　to　identify　the　road　type　from　remote　sensing　imagery,　and　road　types　depend　largely　on　the　characteristics　of　roads.　Thus,　how　to　extract　road　features　and　further　making　road　classification　efficient　have　become　a　popular　and　challenging　research　topic.　In　this　paper,　we　propose　a　road　recognition　method　for　remote　sensing　imagery　using　incremental　learning.　In　principle,　our　method　includes　the　following　steps:　1)　the　non-road　remote　sensing　imagery　is　first　filtered　by　using　support　vector　machine;　2)　the　road　network　is　obtained　from　the　road　remote　sensing　imagery　by　computing　multiple　saliency　features;　3)　the　road　features　are　extracted　from　road　network　and　background　environment;　and　4)　the　roads　are　recognized　as　three　road　types　according　to　the　classification　results　of　incremental　learning　algorithm.　The　experimental　results　show　that　our　method　has　higher　road　recognition　rate　as　well　as　less　recognition　time　than　the　other　popular　algorithms.