# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 4 Experiment
### Comparative Experiments  
We implemented the coastal zone change detection model in PyTorch, using ImageNet-pretrained ResNet as the backbone. The input size was set to 256 pixels for training, and the AdamW optimizer was used for parameter optimization. After adjusting the parameters, the batch size for training and testing was set to 8, the initial learning rate to 0.0001, and the weight decay coefficient to 0.01. The experiments were conducted on an NVIDIA Tesla A100 SXM2 40 GB, with each experiment trained for 150 epochs. Validation was performed after each epoch, and the best model was selected for evaluation on the test set. To demonstrate the effectiveness of our model, we compared it with several leading change detection models from the past five years, using both our self-built coastal zone change detection dataset and the public LEVIR_CD dataset. Our model incorporates a high-frequency attention module, a spatio-temporal difference attention module, and a foreground attention module, all based on a multi-scale Siamese network while also optimizing the loss function. After 150 iterations, the model accuracy was obtained, as  
Figure 10: Sample image from the BCZ_CD dataset. Figure 9: Flow chart of experimental data preprocessing. shown in Tables 2 and 3. Figure 11 present a comparison of accuracy for the different methods on the coastal zone change detection dataset (BCZ_CD) in the form of line charts. Figure 12 shows a typical example of the detection results for different methods on the coastal zone change detection dataset. Figure 13 presents a comparison of accuracy for the different methods on the public change detection dataset (LEVIR_CD) in the form of a line chart. Tables 2 and 3 compare the performance of the AMMNet model with several existing change detection models, including DTCDSCN, MSCANet, and AMTNet, on two datasets. First, on BCZ_CD, as shown in Table 2, the AMMNet model performs well across all metrics, particularly in IoU (90.263%), precision (94.605%), and recall (95.161%). The F1 score reaches 94.882%, indicating high accuracy in coastal zone change detection. AMMNet leads in the IoU metric, indicating greater accuracy in capturing actual change areas, better identifying true changes, and minimizing false alarms. High scores in precision and recall show that AMMNet achieves an optimal balance between the two, ensuring accurate predictions while minimizing false negatives. The F1 score further confirms AMMNet's superiority in overall detection capability, reflecting its ability to handle complex change scenarios. These results show that AMMNet performs well in specific scenarios and demonstrates broad adaptability for other change detection tasks. Secondly, as shown in Table 3 and Figure 13, the AMMNet model performs well on the LEVIR_CD dataset, remaining leading in key metrics such as the IoU, precision (Pr), recall (Rc), and F1, with the F1 score, reaching 91.104%. This further proves AMMNet's adaptability and robustness across various scenarios. The LEVIR_CD dataset is widely used as a benchmark for evaluating model performance in complex scenarios due to its diversity and extensive usage. AMMNet maintains high Pr and Rc values, demonstrating strong detection capabilities and accuracy across different scenarios, suggesting that AMMNet has strong generalization ability. In the initial experiments, using only the HFAM, the model achieved an IoU of 81.411%, indicating that it could capture key features but still showed limitations in fine-grained change detection. With the SDAM, performance improved, with an IoU of 84.328%, precision of 90.815%, recall of 92.191%, and an F1 score of 91.498%. This suggests that the SDAM helps the model balance precision and comprehensiveness, particularly improving recall and the F1 score, allowing for better identification of the change regions and reducing detection leakage. Adding the FAM improved the IoU to 88.239%, with the precision and recall at 92.850% and 94.672%, and the F1 score at 93.752%.