Aerial　scene　classification,　aiming　at　assigning　a　specific　semantic　class　to　each　aerial　image,　is　a　fundamental　task　in　the　remote　sensing　community.　Aerial　scene　images　have　more　diverse　and　complex　geological　features.　While　some　statistics　of　images　can　be　well　fit　using　convolution,　it　limits　such　models　to　capturing　the　global　context　hidden　in　aerial　scenes.　Furthermore,　to　optimize　the　feature　space,　many　methods　add　class　information　to　the　feature　embedding　space.　However,　they　seldom　combine　model　structure　with　class　information　to　obtain　more　separable　feature　representations.　In　this　article,　we　propose　to　address　these　limitations　in　a　unified　framework　(i.e.,　CGFNet)　from　two　aspects:　focusing　on　the　key　information　of　input　images　and　optimizing　the　feature　space.　Specifically,　we　propose　a　global-group　attention　module　(GGAM)　to　adaptively　learn　and　selectively　focus　on　important　information　from　input　images.　GGAM　consists　of　two　parallel　branches:　the　adaptive　global　attention　branch　(AGAB)　and　the　region-aware　attention　branch　(RAAB).　AGAB　utilizes　an　adaptive　pooling　operation　to　better　model　the　global　context　in　aerial　scenes.　As　a　supplement　to　AGAB,　RAAB　combines　grouping　features　with　spatial　attention　to　spatially　enhance　the　semantic　distribution　of　features　(i.e.,　selectively　focus　on　effective　regions　of　features　and　ignore　irrelevant　semantic　regions).　In　parallel,　a　focal　attention　loss　(FA-Loss)　is　exploited　to　introduce　class　information　into　attention　vector　space,　which　can　improve　intraclass　consistency　and　interclass　separability.　Experimental　results　on　four　publicly　available　and　challenging　datasets　demonstrate　the　effectiveness　of　our　method.