## 5 Conclusions  
For the task of remote sensing change detection, this paper proposes a novel network, MDNet, which constructs a complete optimization framework of \"multi-scale feature extraction--cross-scale aggregation--difference enhancement--context modeling\". The core components of this network include the EMF, CASAM, DPEM, Transformer, and CAM. The EMF and CASAM strengthen multi-scale semantic representation capabilities through a parallel collaborative mechanism. By means of deformable convolution in  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **FLOPs(G)** & **Params(M)** \\ \hline FC-EF & 3.58 & 1.35 \\ SNUNet & 54.83 & 12.03 \\ BiT & 8.75 & 3.49 \\ AMTNet & 21.56 & 24.67 \\ WS-Net++ & 40.59 & 906.16 \\ MDNet (Ours) & 26.43 & 36.62 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model efficiency comparison table.  
pyramid structures and cross-level feature interaction, they achieve fine-grained modeling of change targets of different sizes. The DPEM and Transformer collaboratively enhance global-local coupled modeling of spatio-temporal differences. The former strengthens spectral-structural differences by constructing a differential feature space, while the latter captures long-range dependencies using self-attention mechanisms, forming multi-level feature expressions from local differences to global associations. The CAM adaptively suppresses redundant information and enhances discriminative feature responses through a channel-wise feature screening mechanism, further optimizing the semantic consistency of model outputs. Based on a series of quantitative and qualitative comparisons on three popular change detection datasets, the superiority, robustness, and generalizability of MDNet are demonstrated. Our future research intends to focus on two aspects: first, optimizing the network architecture to reduce computational resource consumption while maintaining high accuracy; second, incorporating semi-supervised paradigms inspired by WS-Net++-specifically pseudo-label propagation and cross-scale consistency constraints--to enhance MDNet's adaptability in small-data regimes like CLCD, thereby reducing annotation dependency while maintaining detection fidelity.  
Conceptualization, J.L.; Methodology, J.L. and M.Z.; Software, M.Z. and Z.Y.; Validation, X.W.; Formal analysis, Y.S.; Resources, Q.W.; Data curation, Y.S. and Q.W.; Writing - original draft, M.Z.; Writing - review & editing, M.Z. and X.W.; Visualization, Y.S. and Z.Y.; Supervision, J.L.; Project administration, J.L.; Funding acquisition, X.W. and Q.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Guangxi Real-Scene 3D Digital and Intelligent Collaborative Production and Application Technology System project (No. 2024 ZRBSHZ155), and the Guangxi Key Laboratory of Spatial Information and Geomatics Program (No. 21-238-21-24).  
The original data presented in the study are openly available at [[https://justchenhao.github.io/LEVIR/](https://justchenhao.github.io/LEVIR/)]([https://justchenhao.github.io/LEVIR/](https://justchenhao.github.io/LEVIR/)) (LEVIR-CD dataset, accessed on 18 January 2025), [[https://github.com/linyiyuan11/AMT_Net](https://github.com/linyiyuan11/AMT_Net)]([https://github.com/linyiyuan11/AMT_Net](https://github.com/linyiyuan11/AMT_Net)) (WHU-CD dataset, accessed on 20 January 2025), and [[https://github.com/liumency/CropLand-CD](https://github.com/liumency/CropLand-CD)]([https://github.com/liumency/CropLand-CD](https://github.com/liumency/CropLand-CD)) (CLCD dataset, accessed on 3 February 2025).  
The authors declare no conflicts of interest.