## 6 Conclusions  
This paper proposes a Multi-Scale Attention Siamese Network that integrates Convolutional Neural Networks (CNNs) and Transformers, targeting farmland change detection in high-resolution remote sensing imagery. The proposed network incorporates a Multi-Scale Mixer and a Cross-Channel Spatial Encoder, enabling the joint modeling of local details and global spatial dependencies. This design significantly enhances the network's ability to capture multi-scale representations of complex surface changes. Furthermore, the Multi-Scale Attention Decoder effectively improves the fusion quality and robustness of multi-level features. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches on both the CLCD and HRSCD datasets, especially in terms of recall, highlighting its superior capability in capturing real change targets, thereby providing strong support for reducing missed detections in farmland change monitoring. Despite these promising results, the proposed approach still has certain limitations, particularly regarding the interpretability and stability of attention weights. Existing attention mechanisms typically derive weight distributions through feature similarity or weighted aggregation operations, but such distributions often lack clear physical or semantic interpretations. In farmland change detection tasks, the spatial positions, feature channels, and scale preferences reflected by attention weights are not explicitly aligned with the intrinsic laws of farmland change. This results in a \"black-box\" decision process, which limits transparent evaluations and reliable reasoning in practical applications. Moreover, the attention allocation process is susceptible to factors such as the training data distribution and noise interference, leading to noticeable variations in attention patterns across different samples. This instability further weakens the model's generalization performance. In addition, the training and detection performance of the current model heavily rely on the quality and distributional balance of annotated data. For example, the model trained on the CLCD dataset achieves significantly better performance than that trained on HRSCD, revealing limited cross-region adaptability, which remains a challenge for future work. From the application perspective, the core objective of farmland change detection is to develop an efficient and automated monitoring and early warning system for agricultural land use changes, providing scientific evidence and decision-making support for farmland protection, land use supervision, and illegal land occupation monitoring. The proposed MSA-SiamNet not only achieves a high pixel-wise recognition accuracy but also lays a solid foundation for the practical deployment and systematic development of change detection technologies in the future. Future research could focus on cross-region adaptation, multi-source information fusion, real-time monitoring and early warning integration, and edge deployment. Specifically, to address variations in farmland landscapes, crop types, and cultivation patterns across regions, future studies could incorporate few-shot transfer learning and self-supervised pretraining techniques to enhance cross-region generalization and reduce dependency on high-quality annotated data. Additionally, by integrating optical and SAR imagery with parcel management data and other heterogeneous information sources, the model's capacity to analyze change types can be further improved. This could support the development of an intelligent monitoring and information service platform for agricultural regulatory agencies, enabling fully automated change reporting, dynamic classification-based early warning, policy response, and farmer feedback, thus advancing change detection technologies from algorithmic breakthroughs to systematic applications.