Existing　deep　models　for　facade　parsing　often　fail　in　classifying　pixels　in　heavily　occluded　regions　of　facade　images　due　to　the　difficulty　in　feature　representation　of　these　pixels.　In　this　paper,　we　solve　facade　parsing　with　occlusions　by　progressive　feature　learning.　To　this　end,　we　locate　the　regions　contaminated　by　occlusions　via　Bayesian　uncertainty　evaluation　on　categorizing　each　pixel　in　these　regions.　Then,　guided　by　the　uncertainty,　we　propose　an　occlusion-immune　facade　parsing　architecture　in　which　we　progressively　re-express　the　features　of　pixels　in　each　contaminated　region　from　easy　to　hard.　Specifically,　the　outside　pixels,　which　have　reliable　context　from　visible　areas,　are　re-expressed　at　early　stages;　the　inner　pixels　are　processed　at　late　stages　when　their　surroundings　have　been　decontaminated　at　the　earlier　stages.　In　addition,　at　each　stage,　instead　of　using　regular　square　convolution　kernels,　we　design　a　context　enhancement　module　(CEM)　with　directional　strip　kernels,　which　can　aggregate　structural　context　to　re-express　facade　pixels.　Extensive　experiments　on　popular　facade　datasets　demonstrate　that　the　proposed　method　achieves　state-of-the-art　performance.