Hyperspectral　images　(HSI)　classification　is　inherently　challenged　by　the　degraded　spatial　information　in　complex　scenarios,　where　existing　feature　extraction　methods　fail　to　realize　the　consistent　compensation　from　spectral　to　spatial　information.　To　address　this,　we　propose　a　novel　framework　that　integrates　Local　Binary　Patterns　(LBP)　for　texture-guided　spatial　enhancement　and　a　Polarity　Transformer　to　model　complementary　feature　representations.　The　key　innovation　lies　in　decomposing　the　input　features　into　polarity　mask　through　the　LBP　texture　guidance.　The　positive　polarity　emphasizes　dominant　spectral-spatial　correlations,　while　the　negative　suppresses　noise　and　redundant　responses,　jointly　enabling　adaptive　compensation　of　lost　spatial　details.　Simultaneously,　multi-scale　LBP　operators　are　embedded　prior　to　the　Transformer　to　explicitly　encode　rotation-invariant　texture　features.　The　original　HSI　data　and　LBP-enhanced　features　are　processed　by　polarity　Transformers　and　fused　through　a　cross-polarity　interaction　module,　ensuring　the　complementary　advantages　of　global　spectral　context　and　local　texture　details.　Experimental　results　on　widely-used　HSI　datasets　demonstrate　that　our　method　achieves　superior　classification　performance　compared　to　state-of-the-art　approaches,　particularly　in　low-resolution　conditions.　©　2004-2012　IEEE.