Band　selection　is　a　crucial　task　in　the　dimensionality　reduction　of　hyperspectral　remote　sensing　imagery.　Its　objective　is　to　choose　a　subset　of　bands　with　minimal　redundant　information,　high　information　content,　and　class　discriminability.　To　address　the　issues　of　existing　band　selection　methods　based　on　nearest　neighbor　subspace　partitioning,　which　do　not　consider　the　spatial　distribution　of　objects　and　neglect　the　effect　of　noisy　bands　when　computing　cluster　centers,　this　study　proposes　a　hyperspectral　image　band　selection　method　that　integrates　a　spatial-spectral　structure　with　improved　local　density,　referred　to　as　ISSS-ELD.　This　method　first　performs　image　segmentation　on　the　hyperspectral　image　using　an　entropy-based　approach　to　acquire　homogeneous　regions.　A　composite　region-level　neighboring　band　correlation　coefficient　vector　is　obtained　by　integrating　the　correlation　coefficient　matrix　of　these　homogeneous　regions.　Subsequently,　a　Gaussian　kernel　is　applied　to　globally　smooth　the　neighboring　band　correlation　coefficient　vector,　reducing　the　influence　of　noisy　bands.　Bands　are　grouped　on　the　basis　of　extremum　points　in　the　smoothed　vector.　The　product　of　the　maximized　improved　local　density　and　band　information　entropy　serves　as　the　criterion　for　selecting　representative　bands.　This　study　conducts　experiments　on　hyperspectral　image　datasets,　including　Indian　Pines,　Botswana,　and　Salinas.　Different　band　selection　methods　are　evaluated　by　calculating　metrics　such　as　classification　accuracy,　average　correlation　coefficient,　and　noise　robustness　of　the　selected　bands.　The　results　are　as　follows:　(1)　Compared　with　pixel-level　correlation-based　partitioning　methods,　the　utilization　of　region-level　correlation　coefficients　results　in　more　reasonable　grouping　of　neighboring　bands,　reducing　band　redundancy　while　retaining　some　potential　characteristic　bands.　The　classification　performance　on　the　three　datasets　is　improved　by　2.63%,　0.68%,　and　0.16%.　(2)　In　contrast　with　methods　solely　using　information　entropy　for　band　assessment,　the　proposed　approach　of　maximizing　the　product　of　improved　local　density　and　information　entropy　proves　effective.　On　the　three　datasets,　the　Overall　Accuracy　(OA)　is　increased　by　4.13%,　0.5%,　and　0.21%.　(3)　Compared　with　six　other　advanced　band　selection　methods,　the　proposed　method　achieves　significant　performance　improvements:　OA　is　increased　from　62.34%　to　75.03%,　from　86.74%　to　88.28%,　and　from　86.04%　to　92.36%　on　the　three　datasets.　Furthermore,　the　selected　subset　of　bands　by　our　method　is　dispersed,　concentrating　in　regions　with　higher　information　entropy　and　effectively　avoiding　the　inclusion　of　noisy　bands.　In　summary,　the　band　subset　selected　by　the　proposed　band　selection　method　exhibits　low　redundancy,　high　information　content,　strong　class　separability,　and　robustness　against　noise,　effectively　addressing　the　challenges　in　hyperspectral　image　band　selection.　©　2025　Science　Press.　All　rights　reserved.