Hyperspectral　remote　sensing　is　a　key　technology　for　remotely　obtaining　the　physical　parameters　of　ground　objects　and　realizing　fine　identification.　It　can　not　only　get　geometrical　properties　of　the　target　scenes　but　also　obtain　radiance　that　reflects　the　characteristics　of　ground　objects.　With　the　development　of　hyperspectral　remote　sensing　data　to　unprecedented　spatial,　spectral,　temporal　resolution　and　large　data　volume,　how　to　adapt　to　the　requirements　of　massive　data　and　achieve　efficient　and　rapid　processing　of　hyperspectral　remote　sensing　data　has　become　the　current　research　focus.　Researchers　are　introducing　scene　classification　into　hyperspectral　image　classification,　integrating　the　spatial　and　spectral　information　to　obtain　semantic　information　oriented　to　larger　observation　units.　However,　almost　all　existing　multispectral/hyperspectral　scene　classification　datasets　have　a　number　of　limitations,　including　inconsistent　spectral　and　spatial　resolutions　or　spatial　resolutions　too　large　to　meet　the　needs　of　fine-grained　classification.　Based　on　the　hyperspectral　images　of　Xi’an　taken　by　the　＇Zhuhai-1＇　constellation,　we　combine　the　result　of　unsupervised　spectral　clustering　and　Google　Earth　to　establish　a　hyperspectral　satellite　image　scene　classification　dataset　named　HSCD-ZH　(Hyperspectral　Scene　Classification　Dataset　from　Zhuhai-1).　It　consists　of　737　images　divided　into　six　categories:　urban,　agriculture,　rural,　forest,　water,　and　unused　land.　Each　image　with　a　size　of　64　×　64　pixels　consists　of　32　bands　covering　the　wavelength　in　the　range　of　400—1000　nm.　In　addition,　we　conduct　spatial-based　and　spectral-based　experiments　to　analyze　the　performance　of　existing　datasets,　and　the　benchmark　results　are　reported　as　a　valuable　baseline　for　subsequent　research.　We　choose　false-color　image　for　the　spatial-based　experiments　and　then　use　popular　deep　and　non-deep　learning　scene　classification　techniques.　In　the　experiments　based　on　spectral,　the　spectral　vectors　at　the　pixel　are　directly　used　as　local　spectral　features,　and　BoVW,　IFK,　and　LLC　are　used　to　encode　them　to　generate　global　representations　for　the　scene.　Using　SVM　as　the　classifier,　the　optimal　overall　classification　achieved　by　the　two　experiments　on　the　proposed　dataset　is　92.34%　and　88.96%,　respectively.　Considering　that　those　methods　have　a　large　amount　of　information　loss,　we　cascade　the　features　extracted　by　the　two　approaches　to　generate　spatial-spectral　features.　The　highest　overall　accuracy　obtained　reaches　94.64%,　which　is　the　highest　improvement　in　overall　accuracy　compared　to　the　other　datasets.　We　construct　HSCD-ZH　by　effectively　exploiting　both　spectral　and　spatial　features　of　hyperspectral　images,　selecting　various　scenes　that　either　have　representative　spectral　compositions,　clear　spatial　textures,　or　both.　It　has　the　advantages　of　big　intraclass　diversity,　strong　scalability,　and　adapting　to　satellite　hyperspectral　intelligent　information　extraction　requirements.　Both　dataset　and　experiments　can　provide　effective　data　support　for　remote　sensing　scene　classification　research　in　the　hyperspectral　field.　Meanwhile,　experiments　can　indicate　that　extracting　features　based　on　spatial　or　spectral　misses　a　large　amount　of　available　information,　and　integrating　the　features　extracted　by　the　two　methods　can　compensate　for　this　deficiency.　In　our　future　work,　we　aim　to　expand　the　number　of　categories　and　images　of　HSCD-ZH　and　continue　to　explore　algorithms　for　integrating　spatial　and　spectral　information　that　can　accelerate　the　interpretation　and　efficient　exploitation　of　hyperspectral　scene　cubes.　©　2024　Science　Press.　All　rights　reserved.