Texture　analysis　has　received　great　attention　in　the　interpretation　of　high-resolution　satellite　images.　This　paper　aims　to　find　optimal　filters　for　discriminating　between　residential　areas　and　other　land　cover　types　in　high　spatial　resolution　satellite　imagery.　Moreover,　in　order　to　reduce　the　blurring　border　effect,　inherent　in　texture　analysis　and　which　introduces　important　errors　in　the　transition　areas　between　different　texture　units,　a　classification　procedure　is　designed　for　such　high　spatial　resolution　satellite　images　as　follows.　Firstly,　residential　areas　are　detected　using　Gabor　texture　features,　and　two　clusters,　one　a　residential　area　and　the　other　not,　are　detected　using　the　fuzzy　C-Means　algorithm,　in　the　frequency　space　based　on　Gabor　filters.　Sequentially,　a　mask　is　generated　to　eliminate　residential　areas　so　that　other　land-cover　types　would　be　classified　accurately,　and　not　interfered　with　the　spectrally　heterogeneous　residential　areas.　Afterwards,　other　objects　are　classified　using　spectral　features　by　the　MAP　(maximum　a　posterior)　-　ICM　(iterated　conditional　mode)　classification　algorithm　designed　to　enforce　the　spatial　constraints　into　classification.　Experimental　results　on　high　spatial　resolution　remote　sensing　data　confirm　that　the　proposed　algorithm　provide　remarkably　better　detection　accuracy　than　conventional　approaches　in　terms　of　both　objective　measurements　and　visual　evaluation.