Change　detection　(CD)　has　a　significant　application　in　the　remote　sensing　field.　Because　of　the　popularity　of　hyperspectral　image　(HSI)　and　the　application　of　deep　learning　methods,　hyperspectral　image　change　detection　(HSI-CD)　techniques　have　been　greatly　developed.　Among　them,　convolutional　neural　network　(CNN)　has　garnered　the　greatest　interest　in　HSI-CD　due　to　their　superior　feature　learning　capabilities.　However,　current　CNN-based　algorithms　have　trouble　capturing　spectral　similarity　and　long-range　dependency　owing　to　their　intrinsic　structural　restrictions.　Recently,　transformers　have　been　shown　to　extract　global　dependency　from　nature　images　in　an　extremely　efficient　way.　But　it　has　some　difficulties　in　handling　high-dimensional　data,　such　as　HSI.　To　address　these　issues,　we　propose　an　improved　multi-scale　and　spectral-wise　transformer　(MS-SWT).　The　proposed　MS-SWT　is　capable　of　capturing　spectral　similarity　and　long-range　dependence　between　bands　to　enhance　the　efficiency　of　the　HSI-CD　task.　Furthermore,　to　maximize　the　utilization　of　spatial　information,　we　present　a　multi-scale　feature　fusion　module　(MFFM)　to　extract　and　fuse　different　dimensions　of　spatial　features.　More　importantly,　a　locality　self-attention　(LSA)　module　is　employed　to　alleviate　the　problem　of　smoothing　the　distribution　of　attention　scores　due　to　the　large　number　of　spectral　embeddings.　Moreover,　we　design　a　channel　self-supervised　loss　function　that　can　capture　intrinsic　information　from　the　spectral　channels　to　further　strengthen　the　robustness　of　model　training　when　the　training　samples　are　scarce.　Lastly,　comprehensive　experiments　present　the　high　performance　of　our　MS-SWT　on　four　bitemporal　HSI　datasets　and　demonstrate　the　superiority　of　MS-SWT　over　state-of-the-art　approaches.