Recent advances in deep learning [8] for change detection have achieved significant progress through encoder-decoder architectures that leverage weight-shared, pre-trained backbones to extract multi-temporal features [14; 20; 21; 22]. Researchers enhance robustness via multi-task learning [23; 24], training constraints [25; 26; 27], and deep supervision [19], with innovations including pyramid networks for salient feature refinement [28], multi-scale fusion frameworks like MSFF-CDNet [29], and temporal modeling with P2V-CD [18]. SEIFNet further integrates global-local cues to refine boundaries [30], while deep supervision strategies suppress noise through multi-level supervision [31], atrous spatial pyramid pooling [32], attention-based fusion [33], and GAN constraints [34]. However, despite these efforts, Siamese CD frameworks remain limited by unsystematic designs, sensitivity to interference, and insufficient utilization of advanced structures. Crucially, existing methods often neglect comprehensive constraints on bi-temporal backbone features, failing to ensure comparable and identically distributed representations during Siamese feature extraction [1; 2; 19], which undermines their robustness in complex scenarios. A persistent challenge in optical remote sensing CD is visual style interference caused by radiometric, seasonal, and phenological variations [20; 35]. While specialized datasets like LEVIR-CD+ [9] and S2 Looking [10] improve adaptability, feature enhancement strategies dominate current solutions, including difference amplification [12], multi-level fusion [19], and global semantic integration [30]. Techniques such as multi-level difference enhancement [36] and similarity-aware attention networks [37] aim to suppress false positives, yet critical gaps persist: (1) inadequate constraints during Siamese feature extraction hinder consistent feature contrast in complex backgrounds, and (2) robust protocols for quantifying style interference errors remain underdeveloped. Traditional and deep learning methods struggle to disentangle style variations from genuine changes [7], while existing datasets lack real-world heterogeneity [11; 13]. This limitation perpetuates false detections in scenarios with large visual style differences (Figure 1), underscoring the need for systematic feature constraints and standardized evaluation metrics [14; 15; 16; 17; 18]. To address the challenges in robustly measuring background similarity and quantitatively evaluating errors, we propose the Representation Consistency Change Detection (RCCD) framework. This framework is explicitly designed to handle remote sensing images with cross-visual style variations, aiming to achieve robust CD by enforcing representation consistency between bi-temporal global style and local spatial consistency of features throughout the encoding and decoding pipeline. RCCD comprises an encoder, a decoder, and a deep supervisor. The encoder utilizes a pre-trained network with shared weights for multi-level feature extraction. The Unified Decoder and Detector (UDD), equipped with skip connections and residual units, performs scale-wise decoding to generate the CD map. The Representation Consistency (RC) deep supervisor, active only during training, operates at all scales throughout the encoding and decoding process. It consists of local spatial contexts based on the large kernel convolution supervised (LS) module and the Global content-aware Style transfer (GS) module, constraining feature representation consistency includes local context and global style promoting effective CD. The LS module comprehensively assesses correlations between background and target regions in the bi-temporalfeatures. The GS module computes the similarity of channel-wise auto-correlation Gram matrices, implicitly performing style distribution transfer to harmonize global feature representations.