Hyperspectral　images(HSIs)　provide　hundreds　of　narrow　spectral　bands　for　the　land-covers,　thus　can　provide　more　powerful　discriminative　information　for　the　land-cover　classification.　However,　HSIs　suffer　from　the　curse　of　high　dimensionality,　therefore　dimension　reduction　and　feature　extraction　are　essential　for　the　application　of　HSIs.　In　this　paper,　we　propose　an　unsupervised　feature　extraction　method　for　HSIs　using　combined　low　rank　representation　and　locally　linear　embedding　(LRR　LLE).　The　proposed　method　can　simultaneously　use　both　the　spectral　and　spatial　correlation　within　HSIs,　with　LRR　modelling　the　intrinsic　property　of　union　of　low-rank　subspaces　and　LLE　considering　the　correlation　within　spatial　neighbours.　Experiments　are　conducted　on　real　HSI　datasets　and　the　classification　results　demonstrate　that　the　features　extracted　by　LRR　LLE　are　more　discriminative　than　the　state-of-art　methods.　©　2017　IEEE.