Hyperspectral　unmixing　is　one　of　the　most　important　procedures　for　remote　sensing　image　processing.　The　multilayer　non-negative　matrix　factorization　(MLNMF)-based　method　has　been　widely　used　for　hyperspectral　unmixing　due　to　its　good　performance　for　highly　mixed　data　with　multiple-decomposition　structure.　However,　few　works　consider　the　spatial　information　in　the　image,　which　may　enhance　the　performance.　In　order　to　solve　this　issue,　we　propose　a　homogeneous　region　regularized　multilayer　non-negative　matrix　factorization　(HR-MLNMF)　method　for　hyperspectral　unmixing.　In　HR-MLNMF,　the　spatial　information,　depicted　by　the　homogeneous　region,　is　applied　to　regularize　MLNMF,　which　could　enhance　the　smoothness　of　each　homogeneous　spatial　field　to　achieve　better　performance.　Experiments　on　both　synthetic　and　real　datasets　have　validated　the　effectiveness　of　our　method　and　shown　that　it　has　outperformed　several　state-of-the-art　approaches　of　hyperspectral　unmixing.　(C)　2020　Society　of　Photo-Optical　Instrumentation　Engineers　(SPIE)