); [EMAIL_ADDRESS] (W.C.); [EMAIL_ADDRESS] (Y.D.) Ste Key Laboratory of Information Engineering in Surveying, Wuhan University of Science and Technology, Wuhan 430072, China; [EMAIL_ADDRESS] (Y.T. ); [EMAIL_ADDRESS] (W.C.); [EMAIL_ADDRESS] (Y.D.) Ste Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China; [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (S.G.); [EMAIL_ADDRESS] (S.G.); [EMAIL_ADDRESS] (Y.T. ); [EMAIL_ADDRESS] (W.C.); [EMAIL_ADDRESS] (Y.D.) Ste Key Laboratory of Information Engineering, Xi'an 710054, China  
1 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China; [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (S.G.); shunxianiaoIn the era of interconnected satellite constellations [6], the increasing demand for accurate and timely CD from bi-temporal images faces challenges from visual style interferences caused by varying acquisition conditions. These interferences, primarily stemming from radiation differences, atmospheric effects, and seasonal variations, introduce significant difficulties in accurately identifying genuine changes, as shown in Figure 1. These non-change-related pixel value variations manifest as spectral, textural, and semantic discrepancies between images, leading to false detections. For instance, illumination variations can cause shifts in spectral signatures, making similar objects appear different. Atmospheric conditions like haze can weaken textural features, potentially obscuring genuine changes or introducing spurious ones. Moreover, seasonal changes, such as vegetation growth cycles, can lead to substantial semantic changes, even if irrelevant to the user's objective. Consequently, these visual style discrepancies pose a significant obstacle to achieving high accuracy in CD, particularly in measuring the similarity between unchanged and changed areas across spectral, textural, and semantic domains. This necessitates the development of robust CD algorithms that can effectively mitigate the impact of such interferences. Current CD research primarily focuses on mitigating visual style interferences through specialized datasets, sophisticated feature enhancements, and refined deep supervision. However, accurately quantifying background similarity amid complex visual style variations and establishing robust evaluation protocols remain challenging, as exemplified by the false detections arising from complex backgrounds in Figure 1. Traditional and machine learning CD methods struggle with varying visual styles [7], particularly radiation and non-linear weather/phenological changes. Deep learning, such as Siamese networks for CD, has emerged as a dominant approach due to its capacity for high-dimensional semantic understanding [8]. Dedicated visual CD datasets [9; 10] facilitate method adaptation to specific interference patterns, yet they often lack the full spectrum of real-world variations. Methods explicitly addressing visual style interferences primarily leverage feature enhancement strategies, including difference feature enhancement [11; 12], multi  
Figure 1: Six examples include the \(T^{1}\) image, \(T^{2}\) image, false detection map, and label. Red boxes highlight the significant areas of false detection. level feature fusion [13; 14; 15], and global semantic enhancement [16; 17; 18], to suppress false positives. Deep supervision methods, employing techniques like metric learning [9] and multi-scale supervision [19], also play a vital role. However, these approaches often lack effective feature constraints and measurements, hindering accurate similarity measurement in complex backgrounds by failing to establish consistent feature contrast during Siamese feature extraction. Furthermore, robustly quantifying visual style interference errors remains challenging, hindering comprehensive method assessment. Next, we provide a detailed introduction to the progress in related work for these two challenges.