Change　Detection　(CD)　is　the　process　of　recognizing　and　quantitatively　evaluating　changes　in　surface　objects　at　the　exact　location　but　at　different　times　from　remote　sensing　images　that　may　be　caused　by　extreme　heat　events.　Attention-based　CD　methods　have　gained　much　traction　because　of　their　ability　to　concentrate　on　change　regions.　However,　most　current　attention-based　methods　only　calculate　the　attention　matrix　based　on　features　extracted　from　a　single　image　but　fail　to　consider　the　correlation　of　features　extracted　from　images　at　different　times.　The　effectiveness　of　the　correlation　of　learned　features　from　change　regions　in　bi-temporal　images　is　an　essential　element　influencing　the　improvement　of　CD　performance.　This　paper　proposes　a　Cascaded　Attention-Induced　Difference　Representation　Learning　(CADRL)　method　for　multispectral　CD　to　explore　the　correlation　of　features　extracted　from　bi-temporal　images　to　obtain　more　discriminative　features.　The　proposed　CADRL　method　contains　three　modules:　the　feature　extraction　module,　the　Cascaded　Cross-attention　based　Difference　Learning　Module　(CCADLM)　and　the　detection　module.　First,　the　feature　extraction　module　extracts　multi-scale　features　from　bi-temporal　images.　Then,　CCADLM　generates　more　discriminative　features　by　fusing　difference　features　and　the　cross-attention　matrix　learned　from　the　temporal　attention　unit　from　multiple　levels　to　explore　the　correlation　information　of　change　regions　in　bi-temporal　images.　Finally,　the　learned　discriminative　features　are　fed　to　the　detection　module　to　gain　the　final　detection　map.　Experimental　results　on　three　multispectral　datasets　demonstrate　that　the　CADRL　method　outperforms　other　existing　CD　algorithms.