Remote-sensing　(RS)　scene　classification　aims　to　classify　RS　images　with　similar　scene　characteristics　into　one　category.　Plenty　of　RS　images　are　complex　in　background,　rich　in　content,　and　multiscale　in　target,　exhibiting　the　characteristics　of　both　intraclass　separation　and　interclass　convergence.　Therefore,　discriminative　feature　representations　designed　to　highlight　the　differences　between　classes　are　the　key　to　RS　scene　classification.　Existing　methods　represent　scene　images　by　extracting　either　global　context　or　discriminative　part　features　from　RS　images.　However,　global-based　methods　often　lack　salient　details　in　similar　RS　scenes,　while　part-based　methods　tend　to　ignore　the　relationships　between　local　ground　objects,　thus　weakening　the　discriminative　feature　representation.　In　this　article,　we　propose　to　combine　global　context　and　part-level　discriminative　features　within　a　unified　framework　called　CGINet　for　accurate　RS　scene　classification.　To　be　specific,　we　develop　a　light　context-aware　attention　block　(LCAB)　to　explicitly　model　the　global　context　to　obtain　larger　receptive　fields　and　contextual　information.　A　co-enhanced　loss　module　(CELM)　is　also　devised　to　encourage　the　model　to　actively　locate　discriminative　parts　for　feature　enhancement.　In　particular,　CELM　is　only　used　during　training　and　not　activated　during　inference,　which　introduces　less　computational　cost.　Benefiting　from　LCAB　and　CELM,　our　proposed　CGINet　improves　the　discriminability　of　features,　thereby　improving　classification　performance.　Comprehensive　experiments　over　four　benchmark　datasets　show　that　the　proposed　method　achieves　consistent　performance　gains　over　state-of-the-art　(SOTA)　RS　scene　classification　methods.