Hyperspectral　image　classification,　which　assigns　each　pixel　to　predefined　land　cover　categories,　is　of　crucial　importance　in　various　Earth　science　tasks　such　as　environmental　mapping　and　other　related　fields.　In　recent　years,　scholars　have　attempted　to　utilize　deep　learning　frameworks　for　hyperspectral　image　classification　and　achieved　satisfactory　results.　However,　these　methods　still　have　certain　deficiencies　in　extracting　spectral　features.　This　paper　proposes　a　hierarchical　self-attention　network　(HSAN)　for　hyperspectral　image　classification　based　on　the　self-attention　mechanism.　Firstly,　a　skip-layer　self-attention　module　is　constructed　for　feature　learning,　leveraging　the　self-attention　mechanism　of　Transformer　to　capture　contextual　information　and　enhance　the　contribution　of　relevant　information.　Secondly,　a　hierarchical　fusion　method　is　designed　to　further　alleviate　the　loss　of　relevant　information　during　the　feature　learning　process　and　enhance　the　interplay　of　features　at　different　hierarchical　levels.　Experimental　results　on　the　Pavia　University　and　Houston2013　datasets　demonstrate　that　the　proposed　framework　outperforms　other　state-of-the-art　hyperspectral　image　classification　frameworks.　©　2023　Shanghai　Jiaotong　University.　All　rights　reserved.