## 4 Experimental Results and Analysis
The experimental findings, presented in Tables 1 and 2, indicate that conventional Convolutional Neural Networks, specifically DenseNet and SimpleCNN, are ineffective at adequately capturing the background information associated with fire points, resulting in a recall rate of zero and an inability to accurately identify these points. This suggests that Convolutional Neural Networks predominantly focus on the extraction of local features; however, for tasks involving fire point detection, the incorporation of background information and global dependencies is equally essential. In contrast, the Swin Transformer, which is predicated on a self-attention mechanism, is capable of modeling long-range global information, thereby leveraging the surrounding background data of fire points and significantly enhancing detection performance. Furthermore, our proposed model, which amalgamates the extraction of comprehensive fire point features through the integration of LSTM and the Swin Transformer, results in superior performance in terms of accuracy, recall, and F1 score. Conversely, the MLP model primarily depends on the central spectral band information of fire points for detection. While it does not incorporate background information, the central spectral band typically encompasses the most critical features, allowing the MLP to maintain satisfactory performance in accuracy and recall. This observation underscores the notion that, within the context of fire point detection, the core features of the fire points are of greater significance than the background information, which serves merely as supplementary data. Nonetheless, the lack of background information constrains the overall efficacy of the MLP, hindering its competitiveness against our proposed model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **A** & **P** & **R** & **F1** & **ED** & **MD** \\ \hline Our Proposed Method & 0.999 & 0.652 & 0.747 & 0.696 & 0.348 & 0.253 \\ DenseNet & 0.999 & – & 0.000 & – & – & 1.000 \\ SimpleCNN & 0.999 & – & 0.000 & – & – & 1.000 \\ MLP & 0.999 & 0.471 & 0.567 & 0.510 & 0.529 & 0.433 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Province** & **Model** & **A** & **P** & **R** & **F1** & **ED** & **MD** \\ \hline \multirow{4}{*}{Fujian} & Our Proposed Method & 0.999 & 0.700 & 0.764 & 0.731 & 0.300 & 0.236 \\  & DenseNet & 0.999 & – & 0.000 & – & – & 1.000 \\  & SimpleCNN & 0.999 & – & 0.000 & – & – & 1.000 \\  & MLP & 0.999 & 0.617 & 0.640 & 0.628 & 0.383 & 0.360 \\ \hline \multirow{4}{*}{Jiangxi} & Our Proposed Method & 0.999 & 0.604 & 0.730 & 0.661 & 0.396 & 0.270 \\  & DenseNet & 0.999 & – & 0.000 & – & – & 1.000 \\  & SimpleCNN & 0.999 & – & 0.000 & – & – & 1.000 \\  & MLP & 0.999 & 0.324 & 0.495 & 0.392 & 0.676 & 0.505 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of subdivision by province. In general, our suggested approach surpasses the other three methods regarding both the numerical data and visual outcomes. Consequently, our proposed model emerges as the most advantageous option, as it comprehensively utilizes both the core features and background information of fire points.