Models　based　on　convolutional　neural　networks　(CNNs)　have　achieved　remarkable　advances　in　high-resolution　remote　sensing　(HRRS)　images　scene　classification,　but　there　are　still　challenges　due　to　the　high　similarity　among　different　categories　and　loss　of　local　information.　To　address　this　issue,　a　multigranularity　alternating　feature　mining　(MGA-FM)　framework　is　proposed　in　this　article　to　learn　and　fuse　both　global　and　local　information　for　HRRS　scene　classification.　First,　a　region　confusion　mechanism　is　adopted　to　guide　network＇s　shallow　layers　to　adaptively　learn　the　salient　features　of　distinguishing　regions.　Second,　an　alternating　comprehensive　training　strategy　is　designed　to　capture　and　fuse　shallow　local　feature　information　and　deep　semantic　information　to　enhance　feature　representation　capabilities.　In　particular,　the　MGA-FM　framework　can　be　flexibly　embedded　in　various　CNN　backbone　networks　as　a　training　mechanism.　Extensive　experimental　results　and　visualization　analysis　on　three　remote　sensing　scene　datasets　indicated　that　the　proposed　method　can　achieve　competitive　classification　performance.