Accurate　segmentation　of　camouflage　objects　in　aerial　imagery　is　vital　for　improving　the　efficiency　of　UAV-based　reconnaissance　and　rescue　missions.　However,　camouflage　object　segmentation　is　increasingly　challenging　due　to　advances　in　both　camouflage　materials　and　biological　mimicry.　Although　multispectral-RGB　based　technology　shows　promise,　conventional　dual-aperture　multispectral-RGB　imaging　systems　are　constrained　by　imprecise　and　time-consuming　registration　and　fusion　across　different　modalities,　limiting　their　performance.　Here,　we　propose　the　Reconstructed　Multispectral-RGB　Fusion　Network　(RMRF-Net),　which　reconstructs　RGB　images　into　multispectral　ones,　enabling　efficient　multimodal　segmentation　using　only　an　RGB　camera.　Specifically,　RMRF-Net　employs　a　divergent-similarity　feature　correction　strategy　to　minimize　reconstruction　errors　and　includes　an　efficient　boundary-aware　decoder　to　enhance　object　contours.　Notably,　we　establish　the　first　real-world　aerial　multispectral-RGB　semantic　segmentation　of　camouflage　objects　dataset,　including　11　object　categories.　Experimental　results　demonstrate　that　RMRF-Net　outperforms　existing　methods,　achieving　17.38　FPS　on　the　NVIDIA　Jetson　AGX　Orin,　with　only　a　0.96%　drop　in　mIoU　compared　to　the　RTX　3090,　showing　its　practical　applicability　in　multimodal　remote　sensing.　©　2025　China　Ordnance　Society