Due　to　the　various　noises,　the　cloud　image　segmentation　becomes　a　big　challenge　for　atmosphere　prediction.　CNN　is　capable　of　learning　discriminative　features　from　complex　data,　but　this　may　be　quite　time-consuming　in　pixel-level　segmentation　problems.　In　this　paper　we　propose　superpixel　analysis　based　CNN　(SP-CNN)　for　high　efficient　cloud　image　segmentation.　SP-CNN　employs　image　over-segmentation　of　superpixels　as　basic　entities　to　preserve　local　consistency.　SP-CNN　takes　the　image　patches　centered　at　representative　pixels　in　every　superpixel　as　input,　and　all　superpixels　are　classified　as　cloud　or　non-cloud　part　by　voting　of　the　representative　pixels.　It　greatly　reduces　the　computational　burden　on　CNN　learning.　In　order　to　avoid　the　ambiguity　from　superpixel　boundaries,　SP-CNN　selects　the　representative　pixels　uniformly　from　the　eroded　superpixels.　Experimental　analysis　demonstrates　that　SP-CNN　guarantees　both　the　effectiveness　and　efficiency　in　cloud　segmentation.