## 4 Results
### Contrast Experiment  
#### 4.2.1 Quantitative Comparison  
To better verify the generalization performance of the model, the improved model was simultaneously applied to the LEVIR-CD dataset and compared with other classical remote sensing image change detection methods. FC-Siam-conc (Fully Convolutional Siamese Network with Concatenation) [32] leverages the Siamese network structure, comprising two parameter-sharing sub-networks. Each sub-network is a standard convolutional neural network designed to extract features from two periods of remote sensing images. The feature maps of the two images are concatenated to create a fused feature representation, enabling comprehensive utilization of information from both images and facilitating the learning of change patterns. In contrast to traditional fully connected networks, FC-Siam-Conc employs a fully convolutional network architecture, enabling the model to process input images of varying sizes. Subsequently, the fused feature map generates a change detection probability map through additional convolution layers and activation functions, with each pixel value indicating the probability of change at that specific location. The FC-Siam-conc model is trained using supervised learning, typically employing binary cross-entropy or Dice loss as the loss function. Compared to conventional pixel difference-based methods, this model excels in capturing intricate change patterns and holds significant potential for diverse remote sensing applications. [PERSON] et al. proposed SNUNet-CD [33], a densely connected Siamese network for change detection that integrates the Siamese network and NestedUNet. SNUNet-CD addresses the issue of localization information loss in deep neural network layers by ensuring compact information transmission between encoder and decoder, as well as between decoder and decoder. Moreover, an Ensemble Channel Attention Module (ECAM) is introduced for deep supervision. With ECAM, the most representative features from various semantic levels are refined and utilized for the final classification. The characteristics of SNUNet-CD enable it to excel in high-resolution image change detection tasks, exhibiting both high accuracy and robustness. [PERSON] et al. introduced the bi-temporal image transformer (BIT) [34] as a method to efficiently model spatial-temporal contexts. The key changes of interest are represented by semantic tokens, which condense the information into a compact form. By transforming the bitemporal image into tokens and utilizing a transformer encoder, contexts are effectively modeled in the space-time domain. We implement the above networks using their public codes with default hyperparameters. Table 1 shows the results of the comparison of the four methods on the LEVIR dataset. To make the table more readable, the best data is marked in bold. The experimental results in Table 1 demonstrate that the proposed method surpasses all evaluation indices. Specifically, regarding the recall indicator, the method presented in this article exhibits an improvement of approximately 8.35% compared to FC-Siam-conc, 1.36% compared to SNUNet-CD, and 1.4% compared to BIT. This enhancement signifies that our method can effectively identify true positive samples (i.e., regions of change), thereby reducing the occurrence of missed detections. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Re** & **F1** & **IOU** \\ \hline FC-Siam-conc [32] & 81.64 & 77.49 & 64.48 \\ SNUNet-CD [33] & 88.63 & 87.83 & 75.84 \\ BIT [34] & 88.59 & 89.52 & 80.35 \\ Ours & **89.99** & **90.80** & **83.15** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The quantitative comparison results of four methods on the LEVIR-CD dataset. The highest score is marked in bold. All the scores are described in percentages (%). Our method demonstrated the highest performance on the \(F1\) score metric. It showed a 13.31% improvement compared to FC-conc, a 2.97% increase compared to SNUNet-CD, and a 1.28% enhancement compared to BIT.