## 2 Literature Review
This design effectively combines various levels of feature information by integrating detailed low-level features and high-level semantic features. By considering both local and global information, the model improves target identification and localization. The method demonstrates strong performance in object detection tasks, validating the efficacy of feature cross-fusion technology. This module can be seamlessly integrated into the current CNN detection network to enhance both detection accuracy and efficiency. [PERSON], et al. [11] combined the spatiotemporal modeling capabilities of 3D-CNNs with the multi-scale feature integration mechanism of feature cross-fusion to leverage the rich information in remote sensing time series images. They introduced the adjacent level feature cross-fusion module to effectively merge features at different abstract levels, enhancing the identification of changing areas. By incorporating cross-scale feature fusion, the model can capture detailed information about slightly changed areas and semantic features of significantly changed areas, thereby improving overall change detection accuracy. However, the complex network structure and high computational resource and training data requirements may hinder practical deployment and efficiency. While spatiotemporal information integration is achieved, further strategies for enhancing time modeling are needed to fully capture complex dynamic change characteristics in continuous time series. The method primarily focuses on change detection through image contrast and may not effectively handle noise interference, atmospheric condition changes, and other factors affecting remote sensing data quality, necessitating improvements in robustness. To dynamically adjust the importance of different spatiotemporal regions in the input data, researchers have incorporated an attention mechanism module into the network. This module assists the model in concentrating on pertinent information while reducing the impact of irrelevant or noisy features, ultimately enhancing the robustness of the transformation detection model. [PERSON] et al. [27] investigated the benefits of integrating attention mechanisms into high-resolution remote sensing image classification tasks. By incorporating an attention module into the neural network architecture, the model can enhance its ability to learn more effective feature representations. This attention module allows the network to dynamically focus on the most relevant areas and features within the input image, leading to improved classification performance. Extensive experiments were conducted on a large remote sensing image dataset, demonstrating that the attention-enhanced network outperforms traditional convolutional neural networks significantly. The integration of attention mechanisms in remote sensing image classification presents an innovative approach, highlighting the broad applicability of attention mechanisms in this domain. Driven by deep learning technology, significant progress has been made in the field of remote sensing change detection. Specifically, recent advancements in 3D-CNNs and multi-level feature fusion methods have shown promising results in accurately identifying changes in remote sensing images. These advanced technologies can effectively leverage the abundant spatiotemporal information present in satellite images, offering a more efficient solution to the remote sensing change detection challenge and enhancing support for dynamic environmental monitoring and analysis. In comparison to traditional methods, these approaches have notably enhanced accuracy, robustness, and intelligence.