HyperSpectral　Image　(HSI)　classification　methods　based　on　limited　labeled　samples　have　made　significant　progress　in　recent　years.　However,　due　to　the　specificity　of　hyperspectral　images,　redundant　information　and　limited　labeled　samples　pose　great　challenges　for　extracting　highly　discriminative　features.　In　addition,　owing　to　the　uneven　distribution　of　pixels　in　each　category,　how　to　strengthen　the　role　of　central　pixels　and　attenuate　the　negative　impact　of　surrounding　pixels　with　different　categories　is　also　the　key　to　improve　the　classification　performance.　To　overcome　the　above　limitations,　an　HSI　classification　method　based　on　Multi-Scale　Asymmetric　Dense　Network　(MS-ADNet)　is　proposed.　Firstly,　a　multi-scale　sample　construction　module　is　proposed,　which　extracts　multiple　scale　patches　around　each　pixel　and　performs　deconvolution　and　stitching　to　construct　multiscale　input　samples　that　contain　both　detailed　structural　regions　and　large　homogeneous　regions.　Next,　an　asymmetric　densely　connected　structure　is　proposed　to　achieve　kernel　skeleton　enhancement　in　joint　spatial　and　spectral　feature　extraction,　i.e.,　enhancement　of　features　extracted　from　the　central　cross-skeleton　portion　of　a　square　convolutional　kernel,　which　effectively　facilitates　feature　reuse.　Moreover,　to　improve　the　discriminability　of　spectral　features,　a　streamlined　element　spectral　attention　mechanism　is　proposed　and　placed　at　the　front　and　back　ends　of　the　densely　connected　network.　With　only　five　samples　per　class　used　for　network　training,　the　proposed　method　achieves　competitive　classification　results　with　overall　accuracies　of　77.66%,　84.54%,　and　92.39%　on　the　Indiana　Pines,　Pavia　University,　and　Salinas　datasets,　respectively.　©　2024　Science　Press.　All　rights　reserved.