Few-shot　learning　image　classification　(FSLIC)　is　a　task　that　has　gained　enhanced　focus　in　recent　years,　the　cost　of　collecting　and　annotating　large　number　of　data　samples　in　some　specialised　domains　is　expensive,　Few-shot　remote　scene　classification　(FRSSC)　is　of　great　utility　in　scenarios　where　sample　is　scarce　and　labelling　is　extremely　costly,　the　core　problem　of　this　task　is　how　to　identify　new　classes　with　scarce　and　expensive　few-shot　samples.　However,　existing　work　prefers　complicated　feature　extraction　in　various　ways　and　the　enhancement　results　are　not　satisfactory,　this　paper　aims　to　improve　the　effectiveness　of　FSLIC　not　only　through　complicated　feature　extraction　but　also　by　exploring　alternative　approaches.　Here　are　multiple　avenues　to　improve　the　performance　of　few-shot　classifiers.　Training　with　a　scarce　data　in　a　few-shot　learning　(FSL)　task　often　results　in　a　biased　feature　distribution.　In　this　paper,　we　propose　a　method　to　address　this　issue　by　calibrating　the　support　set　data　feature　using　sufficient　base　class　data.　(Our　data　distribution　calibration　method　(DC)　is　on　top　of　feature　extractor),　requiring　no　additional　parameters.　And　the　feature　extraction　model　is　further　optimised　and　the　feature　extractor　of　DC-2DEC　is　optimised　with　the　task　of　dealing　with　the　spatial　context　structure　of　the　image　i.e.　rotation　prediction　pretext,　specifically　rotation　prediction.　We　refer　to　the　proposed　method　as　DC-2DEC,　and　we　apply　it　to　few-shot　learning　classification　in　RS　image　(RS　image)　scene　recognition.　Through　experiments　conducted　on　traditional　few-shot　datasets　and　RS　image　datasets,　we　validate　the　algorithm　and　present　corresponding　experimental　results.　These　results　demonstrate　the　competitiveness　of　DC-2DEC,　highlighting　its　efficacy　in　few-shot　learning　classification　for　RS　images.