In　order　to　solve　the　problems　in　the　traditional　remote　sensing　image　based　on　spectral　information,　such　as　low　classification　accuracy,　different　object　with　the　same　spectral　features　or　the　same　object　with　the　different　spectral　features,　and　limited　sample　quantity　and　so　on,　a　remote　sensing　image　classification　method　based　on　the　support　vector　machine　(SVM)　including　with　textural　features　is　proposed.　Using　Langqi　Island　of　Fuzhou　as　experimental　area,　preprocessing　and　principal　component　analysis　were　made　to　initialize　TM　images,　and　the　spectral　features　and　GLCM-based　textural　features　of　ground　objects　were　extracted　and　analyzed　respectively.　Then,　the　extraction,　training　and　testing　of　samples　based　on　the　two　types　of　features　were　finished　for　training　various　SVM　classifiers,　which　were　used　for　classifying　land　use　in　the　experimental　area.　Through　the　maximum　likelihood　method,　the　BP　neural　network　and　the　support　vector　machine　(SVM),　a　crossed　classification　and　contrast　experiment　was　made　to　two　different　types　of　samples　based　on　the　simple　spectral　features　and　the　features　combined　with　texture　respectively.　The　experimental　results　showed　that　the　SVM　classification　method　including　textural　features　can　effectively　improve　the　accuracy　of　land　use　classification,　and　therefore　it　can　be　promoted　better.　©　(2014)　Trans　Tech　Publications,　Switzerland.