Change　detection　in　the　hyperspectral　imagery　(HSI)　detects　the　changed　pixels　or　areas　in　bitemporal　images.　HSIs　contain　hundreds　of　spectral　bands,　including　a　large　amount　of　spectral　information.　However,　most　of　deep　learning-based　change　detection　methods　did　not　focus　on　the　spectral　dependency　of　spectral　information　in　the　spectral　dimension　and　just　adopted　the　difference　strategy　to　represent　the　correlation　of　learned　features,　which　limited　the　improvement　of　the　change　detection　performance.　To　address　the　above-mentioned　problems,　we　propose　an　end-to-end　change　detection　network　for　HSIs,　named　spectrum-aware　transformer　network　(SATNet),　which　includes　SETrans　feature　extraction　module,　the　transformer-based　correlation　representation　module,　and　the　detection　module.　First,　SETrans　feature　extraction　module　is　employed　to　extract　deep　features　of　HSIs.　Then,　the　transformer-based　correlation　representation　module　is　presented　to　explore　the　spectral　dependency　of　spectral　information　and　capture　the　correlation　of　learned　features　of　bitemporal　HSIs　from　both　the　perspective　of　difference　and　dot-product　operations,　so　as　to　obtain　more　discriminative　features.　Finally,　the　decision　fusion　strategy　in　the　detection　module　is　utilized　to　the　learned　discriminative　features　to　generate　the　final　change　map　for　better　change　detection　performance.　Experimental　results　on　three　hyperspectral　datasets　show　that　the　proposed　SATNet　is　superior　to　the　existing　change　detection　methods.