Dust,　pollutant　and　the　aerosol　particles　in　the　air　bring　significant　challenge　to　the　atmospheric　prediction,　and　the　segmentation　of　millimeter-wave　radar　cloud　image　has　become　a　key　to　deal　with　the　problem.　This　paper　presents　superpixel　analysis　based　cloud　image　segmentation　with　fully　convolutional　networks　(FCN)　and　convolutional　neural　networks　(CNN),　named　FCN-CNN.　Firstly,　the　superpixel　analysis　is　performed　to　cluster　the　neighborhood　of　each　pixel　in　the　cloud　image.　Then　the　cloud　image　is　given　to　the　FCN　with　different　steps,　such　as　FCN　32s　and　FCN　8s.　The　＇non-cloud＇　area　in　the　FCN　32s　result　must　be　a　part　of　the　＇non-cloud＇　area　in　the　cloud　image.　Meanwhile,　the　＇cloud＇　area　in　the　FCN　8s　result　must　be　a　part　of　the　＇cloud＇　area　in　the　cloud　image.　The　remaining　uncertain　region　of　the　cloud　image　needs　to　be　further　estimated　by　CNN.　For　efficiency,　it　is　necessary　to　select　several　key　pixels　in　the　superpixel　to　represent　the　characteristics　of　the　superpixel　region.　The　selected　key　pixels　are　classified　by　CNN　as　＇cloud＇　or　＇non-cloud＇.　The　experimental　results　illustrate　that　while　the　accuracy　of　FCN-CNN　is　almost　equivalent　to　MR-CNN　and　SP-CNN,　the　speed　is　880　times　higher　than　MR-CNN,　and　1.657　times　higher　than　SP-CNN.　©　Copyright　2018,　Institute　of　Software,　the　Chinese　Academy　of　Sciences.　All　rights　reserved.