In　this　paper,　an　innovative　method　of　integrating　hard　and　soft　classification,　extended　support　vector　machines,　is　introduced　to　map　impervious　surface　in　urban-rural　fringe　area,　where　is　experiencing　dramatic　land　cover　change.　The　proposed　method　incorporates　the　variance　texture　information　as　an　effective　indicator　to　represent　the　heterogeneity　of　urban-rural　fringe　and　possesses　the　merits　of　hard　and　soft　classification　to　map　both　pure　pixels　and　mixed　pixels　of　impervious　surface.　The　better　performance　compared　with　conventional　hard　and　soft　method　was　demonstrated　in　urban-rural　fringe　area,　Beijing.　©　2018　IEEE.