Change　detection　is　a　fundamental　yet　challenging　task　in　remote　sensing,　crucial　for　monitoring　urban　expansion,　land　use　changes,　and　environmental　dynamics.　However,　compared　with　common　color　images,　objects　in　remote　sensing　images　exhibit　minimal　interclass　variation　and　significant　intraclass　variation　in　the　spectral　dimension,　with　obvious　scale　inconsistency　in　the　spatial　dimension.　Change　detection　complexity　presents　significant　challenges,　including　differentiating　similar　objects,　accounting　for　scale　variations,　and　identifying　pseudo　changes.　This　research　introduces　a　dual　fine-grained　network　with　a　frequency　Transformer　(named　as　FTransDF-Net)　to　address　the　above　issues.　Specifically,　for　small-scale　and　approximate　spectral　ground　objects,　the　network　employs　an　encoder-decoder　architecture　consisting　of　dual　fine-grained　gated　(DFG)　modules.　This　enables　the　extraction　and　fusion　of　fine-grained　level　information　in　dual　dimensions　of　features,　facilitating　a　comprehensive　analysis　of　their　differences　and　correlations.　As　a　result,　a　dynamic　fusion　representation　of　salient　information　is　achieved.　Additionally,　we　develop　a　lightweight　frequency　transformer　(LFT)　with　minimal　parameters　for　detecting　large-scale　ground　objects　that　undergo　significant　changes　over　time.　This　is　achieved　by　incorporating　a　frequency　attention　(FA)　module,　which　utilizes　Fourier　transform　to　model　long-range　dependencies　and　combines　global　adaptive　attentive　features　with　multi-level　fine-grained　features.　Our　comparative　experiments　across　four　publicly　available　datasets　demonstrate　that　FTransDF-Net　reaches　advanced　results.　Importantly,　it　outperforms　the　leading　comparison　method　by　1.23%　and　2.46%　regarding　IoU　metrics　concerning　CDD　and　DSIFN,　respectively.　Furthermore,　efficacy　for　each　module　is　substantiated　through　ablation　experiments.　The　code　is　accessible　on　https://github.com/LeeThrzz/FTrans-DF-Net.