## 2 Literature Review  
For numerous years, the field of remote sensing image change detection has been extensively researched, leading to the development of various methods to address this complex task [16]. Traditional approaches typically depend on manually crafted features and pixel-level difference techniques, which may struggle to detect intricate changes in extensive images [17]. Recently, deep learning methods have demonstrated encouraging outcomes in change detection tasks, leveraging the representation learning abilities of convolutional neural networks [18; 19; 20; 21]. Early deep learning-based change detection methods initially concentrated on extracting spatial features from a single image patch using 2D-CNNs. [PERSON], [PERSON] et al. [22] introduced a remote sensing image change detection method that combines PCA and 2D-CNNs to enhance change detection performance. By compressing the feature space and extracting key features, this method effectively reduces dimensionality and enhances calculation efficiency. Utilizing principal component analysis, the method identifies key features in the changed area, thereby improving detection accuracy. However, the reliance on a manually designed feature extraction mechanism limits its ability to capture complex change patterns. Moreover, noise and registration errors can have a significant impact, potentially leading to the oversight of subtle changes in information. [PERSON], [PERSON] et al. [23] introduced a novel 2D-CNN approach utilizing contrastive learning, demonstrating several advantages. This method eliminates the need for pre-marking changed areas and effectively leverages a substantial amount of unlabeled remote sensing data for training, ultimately improving the robustness of feature representation. However, this technique solely focuses on extracting spatial features from a single time frame, limiting its ability to fully exploit time series information. Consequently, it may struggle to accurately differentiate between genuine change areas and noise-induced interference, potentially impacting the overall change detection performance. Traditional 2D-CNN methods have made progress in remote sensing change detection, but they struggle to effectively capture temporal dynamics and global context information [24]. To overcome these limitations and enhance accuracy and robustness, re searchers have started exploring the use of more advanced 3D-CNN models. These models extend traditional CNNs by incorporating spatiotemporal volumes. [PERSON], [PERSON], et al. [25] introduced a novel three-dimensional CNN model for action recognition. This model effectively extracts features from spatial and temporal dimensions using three-dimensional convolution, allowing for the capture of motion information across multiple adjacent frames. By generating multiple channels of information from input frames and combining information from all channels in the final feature representation, the developed model demonstrates enhanced performance. Additionally, the authors suggest further improving performance by regularizing the output with advanced features and combining predictions from different models. This method represents a pioneering use of 3D-CNN technology. In addition to exploring advanced network architectures, the researchers investigated various feature fusion strategies to enhance the accuracy of change detection. Multi-level feature fusion techniques aim to integrate features extracted at different levels of abstraction, enabling models to utilize both low-level details and high-level semantics. [PERSON] et al. [26] introduced Feature Cross-Fusion Block technology to amalgamate features from various levels in object detection tasks, thereby improving the model's capability to detect and pinpoint targets. This approach merges low-level detailed features and high-level semantic features through a hierarchical feature representation, effectively combining local and global information to enhance the model's target perception.