Change　detection　is　the　process　of　detecting　and　evaluating　differences　from　bi-temporal　remote　sensing　images.　Deep　learning-based　change　detection　methods　have　become　the　mainstream　approaches　due　to　their　discriminative　features　and　good　change　detection　performance.　However,　most　of　the　existing　deep　learning-based　change　detection　methods　did　not　perform　well　in　detecting　subtle　changes　and　did　not　fully　explore　the　underlying　information　of　features　learned　by　deep　neural　networks.　To　address　the　above-mentioned　problems,　we　propose　an　end-to-end　deep　neural　network　for　multispectral　change　detection,　named　Difference-Enhancement　Triplet　Network　(DETNet).　DETNet　mainly　includes　two　modules:　the　triplet　feature　extraction　module　and　the　difference　feature　learning　module.　First,　the　triplet　feature　extraction　module　uses　the　triple　CNN　as　the　backbone　to　extract　representative　spatial-spectral　features.　Second,　the　difference　feature　learning　module　mines　the　underlying　information　of　difference　representations　of　learned　spatial-spectral　features　to　detect　subtle　changes.　Finally,　the　model　employs　a　compound　loss　function,　which　includes　triplet　loss,　contrastive　loss,　and　cross-entropy　loss,　to　guide　DETNet　toward　learning　more　discriminative　features.　Extensive　experimental　results　of　the　proposed　DETNet　and　other　state-of-the-art　methods　on　four　datasets　demonstrate　its　superiority.　IEEE