With　the　development　of　high-resolution　remote　sensing　images　(HR-RSIs)　and　the　escalating　demand　for　intelligent　analysis,　fine-grained　recognition　of　geospatial　objects　has　become　a　more　practical　and　challenging　task.　Although　deep　learning-based　object　recognition　has　achieved　superior　performance,　it　is　inflexible　to　be　directly　utilized　to　the　fine-grained　object　recognition　(FGOR)　tasks　of　HR-RSIs　under　the　limitation　of　the　size　of　geospatial　objects.　An　efficient　fine-grained　object　recognition　method　in　HR-RSIs　from　knowledge　distillation　(KL)　to　filter　grafting　is　proposed.　Specifically,　fine-grained　object　recognition　consists　of　two　stages:　Stage　1　utilizes　oriented　region　convolutional　neural　network　(oriented　R-CNN)　to　accurately　locate　and　preliminarily　classify　geospatial　objects.　At　the　same　time,　it　serves　as　a　teacher　network　to　guide　students＇　effective　learning　of　fine-grained　object　recognition;　in　Stage　2,　we　design　a　coarse-to-fine　object　recognition　network　(CF-ORNet),　as　the　second　teacher　network,　which　realizes　fine-grained　recognition　through　feature　learning　and　category　correction.　After　that,　we　propose　a　lightweight　model　from　knowledge　distillation　to　filter　grafting　on　two　teacher　networks　to　achieve　efficient　fine-grained　object　recognition.　The　experimental　results　on　Vehicle　Detection　in　Aerial　Imagery　(VEDAI)　and　HR　Ship　Collection　2016　(HRSC2016)　datasets　achieve　competitive　performance.