Recently,　remote　sensing　images　have　been　widely　used　in　many　scenarios,　gradually　becoming　the　focus　of　social　attention.　Nevertheless,　the　limited　annotation　of　scarce　classes　severely　reduces　segmentation　performance.　This　phenomenon　is　more　prominent　in　remote　sensing　image　segmentation.　Given　this,　we　focus　on　image　fusion　and　model　feedback,　proposing　a　multi-strategy　method　called　MSAug　to　address　the　remote　sensing　imbalance　problem.　Firstly,　we　crop　rare　class　images　multiple　times　based　on　prior　knowledge　at　the　image　patch　level　to　provide　more　balanced　samples.　Secondly,　we　design　an　adaptive　image　enhancement　module　at　the　model　feedback　level　to　accurately　classify　rare　classes　at　each　stage　and　dynamically　paste　and　mask　different　classes　to　further　improve　the　model＇s　recognition　capabilities.　The　MSAug　method　is　highly　flexible　and　can　be　plug-and-play.　Experimental　results　on　remote　sensing　image　segmentation　datasets　show　that　adding　MSAug　to　any　remote　sensing　image　semantic　segmentation　network　can　bring　varying　degrees　of　performance　improvement.