Hyperspectral　images　(HSIs)　usually　contain　hundreds　of　spectral　bands.　When　they　are　used　for　classification　tasks,　HSIs　may　suffer　from　the　curse　of　high　dimensionality.　To　address　this　problem,　the　essential　procedures　of　dimension　reduction　and　feature　extraction　(FE)　are　employed.　In　this　letter,　we　propose　an　FE　method　for　HSIs　using　low-rank　representation　with　neighborhood　preserving　regularization　(LRR_NP).　The　proposed　method　can　simultaneously　employ　locally　spatial　similarity　and　the　spectral　space　structure,　which　comprises　a　union　of　multiple　low-rank　subspaces.　The　framework　of　LRR　can　structurally　represent　the　union　structure　of　a　spectral　space.　Because　spatial　neighbor　pixels　always　share　high　similarity　in　a　feature　space,　an　NP　regularization　item　is　introduced　into　the　framework　of　LRR　to　consider　the　locally　spatial　correlation.　Classification　experiments　are　conducted　on　real　HSI　data　sets;　the　results　demonstrate　that　the　features　that　are　extracted　by　LRR_NP　are　more　discriminative　than　the　state-of-art　methods,　including　both　unsupervised　methods　and　supervised　methods.