The　problem　of　small　sample　classification　is　to　identify　image　categories　that　have　not　appeared　in　the　training　concentration　when　marking　the　scarce　sample　samples　of　the　training　data　set.　Such　tasks　are　of　great　significance　in　the　recognition　of　remote　sensing　scenarios.　It　is　a　problem　worth　studying　in　this　field.　As　we　all　know,　training　a　deep　learning　model　for　classification　requires　a　considerable　labeling　data　set,　which　makes　the　production　of　training　data　sets　huge.　In　this　article,　we　propose　a　MADB　feature　extraction　model　based　on　Mixed　Attention　Module　as　a　base　model　to　extract　features.　Using　RccaEMD　module　as　the　measurement　algorithm　to　distinguish　the　classification　of　remote　sensing　scenarios.　In　NWPU-RESISC45　dataset,　AID　dataset,　and　UC-Merced　dataset,　it　proves　that　our　method　has　achieved　higher　accuracy　than　the　current　advanced　methods　of　this　field.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.