## 1 Introduction  
As a key technology for identifying surface changes by analyzing differences in temporal remote sensing images [1], remote sensing change detection has been widely applied to various fields such as urban planning [2], ecological environment monitoring [3], and disaster emergency assessment [4]. However, the practical application of this technology still faces multiple technical challenges: first, differences in lighting conditions and changes in sensor parameters during image acquisition easily lead to pseudo-change interference;second, image registration errors and spatial resolution differences affect the precise positioning of change areas; third, the problem of uneven land cover categories significantly restricts the model's generalization ability [5]. In terms of methodological evolution, traditional change detection methods with manually constructed features [6] perform well in simple scenes, but their feature representation capabilities show obvious limitations in complex scenarios. In contrast, deep learning-based paradigms demonstrate significant advantages through end-to-end feature learning mechanisms. For example, [PERSON] et al. [7] used a CNN with dense skip connections to fully learn the feature representations of unlabeled data through multi-level feature reuse. [PERSON] et al. [8] innovatively introduced an edge constraint mechanism to enhance the CNN's perception of change boundaries. Notably, spatio-temporal context modeling has become a key research direction for improving detection accuracy, and researchers have successively integrated feature aggregation or attention mechanisms into CNNs. For instance, [PERSON] et al. [9] and [PERSON] et al. [10] both adopted multi-scale fusion techniques, significantly improving the accuracy of remote sensing change detection by effectively combining feature representations at different scales. [PERSON] et al. [11] constructed an attention-guided edge refinement network, which uses an attention mechanism to aggregate contextual information while introducing an edge refinement module to enhance the structural boundaries of prediction results. [PERSON] et al. [12] incorporated a dual attention mechanism into the change detection network to improve the model's discriminative ability for pseudo-changes. With the breakthrough progress of the Transformer architecture in global modeling, this technology has begun to be introduced into the field of remote sensing change detection. For example, [PERSON] et al. [13] proposed a pure Swin Transformer network, achieving global context awareness through a U-shaped encoder-decoder structure. [PERSON] et al. [14] used Swin Transformer to learn discriminative global features and aggregated multi-level features through a pyramid structure to enhance feature representation. However, the high computational complexity of pure Transformer architectures has prompted research to shift towards the collaborative optimization of convolution and self-attention. Recent studies have proposed several innovative hybrid architectures. For instance, the asymmetric multi-head cross-attention mechanism designed by [PERSON] et al. [15] effectively integrates the local perception advantages of CNNs with the global modeling capabilities of Transformer. The cross-dimensional interactive self-attention module developed by [PERSON] et al. [16] achieves multi-dimensional feature enhancement through joint channel-spatial optimization. Although significant progress has been made in research so far, there is still room for improvement in the dynamic complementary mechanism between local features and global context, computational efficiency optimization, etc. In particular, the balance between multi-scale change detection accuracy and model lightweighting in complex scenarios still requires in-depth exploration.