Change　detection　for　high　resolution　remote　sensing　images　is　an　important　but　challenging　task.　In　this　article,　we　propose　a　spatial-temporal-channel　attention　Unet++　(STC-Unet++)　for　remote　sensing　image　change　detection.　The　STC-Unet++　takes　advantage　of　the　Unet++　structure,　combining　semantic　information　to　change　detection.　In　addition,　it　employs　a　spatial-temporal-channel　attention　mechanism,　extracting　features　more　discriminatively　and　improving　the　change　detection　accuracy　without　increasing　training　time.　Finally,　experiments　are　carried　out　on　the　LEVIR-CD　dataset,　and　the　results　show　that　the　STC-Unet++　can　effectively　detect　the　changes,　achieving　89.0%　recall,　88.3%　accuracy,　88.4%　F1-score,　79.49%　IoU　and　94.1%　AUC.　©　2021　IEEE.