Semantic　segmentation　is　one　of　the　fundamental　tasks　in　understanding　high-resolution　aerial　images.　Recently,　convolutional　neural　network　(CNN)　and　fully　convolutional　network　(FCN)　have　achieved　excellent　performance　in　general　images＇　semantic　segmentation　tasks　and　have　been　introduced　to　the　field　of　aerial　images.　In　this　paper,　we　propose　a　novel　deep　FCN　with　channel　attention　mechanism　(CAM-DFCN)　for　high-resolution　aerial　images＇　semantic　segmentation.　The　CAM-DFCN　architecture　follows　the　mode　of　encoder-decoder.　In　the　encoder,　two　identical　deep　residual　networks　are　both　divided　into　multiple　levels　and　acted　on　spectral　images　and　auxiliary　data,　respectively.　Then,　the　feature　map　concatenation　is　carried　out　at　each　level.　In　the　decoder,　the　channel　attentionmechanism　(CAM)　is　introduced　to　automatically　weigh　the　channels　of　featuremaps　to　perform　feature　selection.　On　the　one　hand,　the　CAM　follows　the　concatenated　feature　maps　at　each　level　to　select　more　discriminative　features　for　classification.　On　the　other　hand,　the　CAM　is　used　to　further　weigh　the　semantic　information　and　spatial　location　information　in　the　adjacent-level　concatenated　feature　maps　for　more　accurate　predictions.　We　evaluate　the　proposed　CAM-DFCN　by　using　two　benchmarks　(the　Potsdam　set　and　the　Vaihingen　set)　provided　by　the　International　Society　for　Photogrammetry　and　Remote　Sensing.　Experimental　results　show　that　the　proposed　method　has　considerable　improvement.