Hyperspectral　unmixing　is　one　of　the　most　important　techniques　in　the　remote　sensing　image　analysis　tasks.　In　recent　decades,　nonnegative　matrix　factorization　(NMF)　has　been　shown　to　be　effective　for　hyperspectral　unmixing　due　to　the　strong　discovery　of　the　latent　structure.　Most　NMFs　put　emphasize　on　the　spectral　information,　but　ignore　the　spatial　information,　which　is　very　crucial　for　analyzing　hyperspectral　data.　In　this　paper,　we　propose　an　improved　NMF　method,　namely　NMF　with　region　sparsity　learning　(RSLNMF),　to　simultaneously　consider　both　spectral　and　spatial　information.　RSLNMF　defines　a　new　sparsity　learning　model　based　on　a　small　homogeneous　region　that　is　obtained　via　the　graph　cut　algorithm.　Thus　RSLNMF　is　able　to　explore　the　relationship　of　spatial　neighbor　pixels　within　each　region.　An　efficient　optimization　scheme　is　developed　for　the　proposed　RSLNMF,　and　its　convergence　is　theoretically　guaranteed.　Experiments　on　both　synthetic　and　real　hyperspectral　data　validate　the　superiority　of　the　proposed　method　over　several　state-of-the-art　unmixing　approaches.