In　hyperspectral　images　(HSIs),　different　land　cover　(LC)　classes　have　distinct　reflective　characteristics　at　various　wavelengths.　Therefore,　relying　on　only　a　few　bands　to　distinguish　all　LC　classes　often　leads　to　information　loss,　resulting　in　poor　average　accuracy.　To　address　this　problem,　we　propose　a　method　called　Cascaded　Spatial　Cross-Attention　Network　(CSCANet)　for　HSI　classification.　We　design　a　cascaded　spatial　cross-attention　module,　which　first　performs　cross-attention　on　local　and　global　features　in　the　spatial　context,　then　uses　a　group　cascade　structure　to　sequentially　propagate　important　spatial　regions　within　the　different　channels,　and　finally　obtains　joint　attention　features　to　improve　the　robustness　of　the　network.　Moreover,　we　also　design　a　two-branch　feature　separation　structure　based　on　spatial-spectral　features　to　separate　different　LC　Tokens　as　much　as　possible,　thereby　improving　the　distinguishability　of　different　LC　classes.　Extensive　experiments　demonstrate　that　our　method　achieves　excellent　performance　in　enhancing　classification　accuracy　and　robustness.