Learning　powerful　and　discriminative　representation　is　critical　for　boosting　the　performance　of　few-shot　remote　sensing　scene　classification　(FSRSSC).　The　remote　sensing　images　have　unique　characteristics,　such　as,　complex　background　and　co-occurrence　of　multiple　objects,　making　FSRSSC　challenging.　To　address　this　problem,　in　this　article,　a　novel　FSRSSC　method　is　proposed.　First,　a　spatial　affinity　attention　(SAA)　mechanism　is　designed　to　encourage　the　network　model　to　focus　on　critical　regions.　The　SAA　infers　attention　maps　from　both　channel　and　spatial　dimensions,　and　encodes　the　mean　values　and　affinities　of　feature　nodes　in　each　channel　along　the　vertical　and　horizontal　directions.　Second,　a　class　surrogate-based　supervised　contrastive　learning　(CSSCL)　strategy　is　proposed　to　promote　intra-class　compactness　and　inter-class　dispersion.　Different　from　supervised　contrastive　learning　(SCL),　the　CSSCL　learns　a　surrogate　for　each　class　in　the　contrastive　space　and　select　the　corresponding　class　surrogate　rather　than　the　samples　from　the　same　class　for　the　anchor　to　form　positive　pairs.　It　can　alleviate　the　impact　of　too　hard　or　too　simple　positive　sample　pairs　on　the　model　generalization　that　exists　when　SCL　is　introduced　into　FSRSSC.　The　model　is　trained　under　the　joint　supervision　of　cross-entropy　(CE)　loss　and　CSSCL　loss　on　the　merged　base　class　data　instead　of　using　a　meta-learning　strategy　to　learn　a　robust　base　feature　extractor.　Extensive　experimentations　on　three　public　remote　sensing　benchmark　datasets　show　that　our　proposed　method　can　achieve　a　competitive　or　state-of-the-art　(SOTA)　classification　performance.