Hyperspectral　image　change　detection　(HSI-CD)　is　a　fundamental　task　in　the　field　of　remote　sensing　(RS)　observation,　which　utilizes　the　rich　spectral　and　spatial　information　in　bitemporal　HSIs　to　detect　subtle　changes　on　the　Earth＇s　surface.　However,　modern　deep　learning　(DL)-based　HSI-CD　methods　mostly　rely　on　patch-based　methods,　which　leads　to　spectral　band　redundancy　and　spatial　information　noise　in　limited　receiving　domains,　thus　ignoring　the　extraction　and　utilization　of　saliency　information　and　limiting　the　improvement　of　CD　performance.　To　address　these　issues,　this　article　proposes　a　joint　saliency　temporal-spatial-spectral　information　network　(STSS-Net)　for　HSI-CD.　The　principal　contributions　of　this　article　can　be　summarized:　1)　we　have　designed　a　spatial　saliency　information　extraction　(SSIE)　module　for　denoising　based　on　distance　from　center　pixels　and　spectral　similarity　of　the　substance,　which　increases　the　attention　to　spatial　differences　between　similar　spectral　substances　and　different　spectral　substances;　2)　we　have　designed　a　compact　high-level　spectral　information　tokenizer　(CHLSIT)　for　spectral　saliency　information,　where　the　high-level　conceptual　information　of　changes　in　spectral　interest　can　be　represented　by　nonlinear　combinations　of　spectral　bands,　and　redundancy　can　be　removed　by　extracting　high-level　spectral　conceptual　features;　and　3)　utilizing　the　advantages　of　CNN　and　transformer　architectures　to　combine　temporal-spatial-spectral　information.　The　experimental　results　on　three　real　HSI-CD　datasets　show　that　STSS-Net　can　improve　the　accuracy　of　CD　and　has　a　certain　improvement　in　the　detection　of　edge　information　and　complex　information.