Road　extraction　from　remote　sensing　image　is　a　very　popular　topic.　There　are　many　methods　dealing　with　this　problem,　but　few　of　them　focus　on　suburban　roads.　This　paper　presents　a　hierarchical　object-based　method　for　suburban　road　extraction.　First,　the　image　is　preprocessed　by　the　Normalized　Difference　Vegetation　Index　(NDVI)　and　the　Hue-Saturation-Intensity　(HSI)　Index.　Vegetation　regions,　shadows　and　water　areas　are　then　removed.　Second,　texture　features　are　applied　to　classifying　the　preprocessed　image　into　two　categories:　road　and　non-road.　Homogeneous　property　is　combined　to　remove　false　classification　so　as　to　generate　initial　road　skeleton.　Third,　the　road　skeleton　is　refined　by　using　a　series　of　morphology　operations　and　shape　features.　Fourth,　the　curvilinear　feature　of　road　skeleton　is　detected　by　a　set　of　multiple　filtering　detectors.　Finally,　a　regression　method　is　performed　to　extract　smooth　and　accurate　road　centerlines.　The　experimental　results　indicate　that　the　proposed　method　is　suitable　for　suburban　road　extraction.　©　2016　ISSN　2185-2766.