## 5 Conclusions  
This study presents a novel network designed for detecting changes in regions of dual-temporal images. The network is specifically developed to tackle issues related to edge integrity caused by noise or registration errors, ultimately improving the accuracy of change detection in remote sensing images. The network incorporates Temporal Concatenation to process the input images, followed by 4 ResNet 3D modules for feature extraction. Subsequently, the feature maps pass through the CAM channel attention mechanism for weighting, and then through four MFCF modules for cross-fusion of different depths. Finally, the Decoder module is used to decode feature information of varying scales. The 3D CNN is employed to extract feature information from two-phase remote sensing images, with the MFCF module effectively fusing feature information from different convolution depths. To enhance module performance, the SE attention mechanism is utilized to ensure that the MFCF module focuses more on the feature information of the evolving region during the fusion of feature information from different convolutional depths. The Decoder module utilized in this study effectively restores the input size without compromising the details of the edge region. By combining BCE and Dice loss functions, the detection accuracy of the altered region is significantly enhanced. The network model is assessed using the LEVIR-CD dataset. In comparison with the other three methods, our network incorporates feature information of varying scales via MFCF, enhancing the overall edge detection completeness and transformation detection accuracy of remote sensing images. This improvement provides more precise data for various applications including forest environment monitoring, land resource management, disaster monitoring and emergency response, urban planning, and traffic management. However, the processing speed of the decoder may face limitations when handling extensive high-resolution data, particularly in resource-constrained environments. Moving forward, our focus will be on enhancing both the accuracy and efficiency of remote sensing image change detection algorithms.  
Figure 12: Comparison of ablation experiments. Rows (**a–d**) showcase four representative samples from the dataset. The first column is the label of the samples. The 2 nd to 4 th columns are the results of the ablation experiment, and the last column is the results of ours.  
**Author Contributions:** Conceptualization, S.Y.; methodology, S.Y., C.T., G.Z. and X.W.; software, S.Y.; validation, S.Y. and Y.X.; formal analysis, S.Y.; investigation, C.T.; resources, C.T.; data curation, S.Y.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y., C.T., G.Z., X.W. and Y.X.; visualization, S.Y., C.T. and Y.X.; supervision, C.T. and X.W.; project administration, C.T.; funding acquisition, C.T. and X.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Plan of China (grant No. 2022 YFF0708500) and the National Natural Science Foundation of China (grant No. 12273040).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
Abbreviations  
Full form  
Change Detection  
Convolutional Neural Networks  
Multi-Level Feature Cross-Fusion  
Channel Attention Mechanism  
3D-CNNs  
3D Convolutional Neural Network  
Squeeze-and-Excitation  
TC  
Temporal Concatenation  
BCE  
Binary Cross-Entropy  
DICE  
Sorensen-Dice  
VHR  
Very High-Resolution  
GE  
Google Earth  
Pr  
Precision  
IOU  
Intersection Over Union  
1 FN  
False Negative  
TP  
True Positive  
FP
False Positive  
Re  
Recall  
De  
Decoder