The　retrieval　of　impervious　surface　information　is　a　hot　topic　in　remote　sensing.　However,　researches　on　impervious　surface　retrieval　from　hyperspectral　remote　sensing　imagery　are　rare.　This　paper　illustrates　a　case　study　of　information　extraction　from　urban　impervious　surfaces　based　on　hyperspectral　remote　sensing　imagery　that　is　intended　to　improve　the　image　spectral　resolution　of　impermeable　materials.　Fuzhou,　Guangzhou,　and　Hangzhou　were　selected　as　test　areas　and　EO-1　Hyperion　images　were　used　as　data　sources.　The　impervious　surface　features　were　retrieved　from　remote　sensing　images　using　linear　spectral　mixture　analysis.　A　stepwise　discriminant　analysis　was　performed　to　select　feature　bands　for　impervious　surface　retrieval.　A　standard　deviation　analysis,　correlation　analysis,　and　principal　component　analysis　were　then　carried　out　for　each　of　those　up　to　158　valid　Hyperion　spectral　bands.　Eleven　feature　bands　were　selected　using　the　stepwise　discriminant　analysis　and　a　new　image　called　Hyperion＇　was　formed.　The　impervious　surface　was　then　retrieved　from　Hyperion＇.　The　results　indicate　that　the　extraction　accuracy　and　coverage　accuracy　are　high　in　all　three　test　areas.　Tests　of　eleven　feature　band　combinations　selected　in　different　areas　show　very　good　representations　of　the　band　combinations　in　impervious　surface　retrieval,　and　can　thus　be　used　as　optimal　band　combinations　for　impervious　surface　retrieval.