Hyperspectral　images　(HSIs)　contain　spatial　features　and　rich　spectral　features　that　provide　them　with　great　advantages　in　target　classification　and　make　it　easy　to　improve　image　classification　accuracy.　Convolutional　neural　networks　(CNNs)　have　shown　good　performance　in　HSI　classification.　However,　blindly　increasing　the　depth　of　the　CNNs　may　lead　to　overfitting.　A　HSI　classification　method　based　on　a　dense　multi-scale　residual　network　is　proposed　to　address　these　two　problems.　The　proposed　framework　obtains　the　spectral-spatial　characteristics　of　HSIs　through　an　improved　multi-scale　residual　network.　Then,　three　cascaded　multi-scale　residual　modules　form　a　deep　network.　The　dense　connection　module　is　used　to　stack　feature　maps　from　all　previous　layers　to　form　the　concatenate　feature　map　rather　than　fusing　pixels　of　these　feature　maps　and　further　achieve　the　purpose　of　improving　classification　accuracy　and　reducing　the　time　consumption　of　the　network.　A　series　of　experiments　show　that　the　proposed　method　achieves　good　experimental　results　on　three　widely　used　hyperspectral　datasets　and　a　new　hyperspectral　dataset　(Farmland　distribution　dataset,　FDD).　©　2022　SPIE.