# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 5 Discussion
### Parametric Analysis  
This section discusses key hyperparameters in the SID-DN method, including coarse detection fusion parameters \(\iota\), the temporal embedding layer in Diffusion \(\gamma_{\text{te}}\), regularization parameters for SID \(\delta\), and background suppression \(\beta\). Firstly, selecting appropriate coarse detection fusion parameters is crucial for obtaining satisfactory detection result \(\iota\), as these parameters significantly influence the distribution of \(\hat{D}\) in Equation (5). Table 6 presents the detection results obtained with different coarse detection ratios across all datasets. As observed in Table 6, the Los datasets achieve maximum performance at a coarse detection ratio of 0.8, while the Gulfport reach their maximum at 0.6 and San Diego datasets reach their maximum at 0.4. This indicates that varying coarse detection fusion ratios can combine the strengths of both methods, thereby enhancing detection results. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset** & **DBACD** & \(\mathcal{L}\_\_\)_sid_ & \(\mathbf{AUC}_{(P_{D},P_{F})}\) & \(\mathbf{AUC}_{(\tau,P_{D})}\) & \(\mathbf{AUC}_{(\tau,P_{F})}\) & \(\mathbf{AUC}_{\text{BS}}\) & \(\mathbf{AUC}_{\text{SNP}}\) \\ \hline
**Los Angeles** & \(\times\) & \(\times\) & 0.9942 & **0.3668** & 0.0028 & 0.9914 & 126.77 \\  & ✓ & \(\times\) & 0.9960 & 0.3295 & 0.0007 & 0.9953 & 430.21 \\  & ✓ & ✓ & **0.9965** & 0.3243 & **0.0006** & **0.9959** & **519.54** \\ \hline
**Gulfport** & \(\times\) & \(\times\) & 0.9992 & **0.3668** & 0.005 & 0.9942 & 63.50 \\  & ✓ & \(\times\) & 0.9993 & 0.3581 & 0.0006 & 0.9987 & 559.50 \\  & ✓ & ✓ & **0.9994** & 0.3282 & **0.0001** & **0.9993** & **1997.28** \\ \hline
**San Diego** & \(\times\) & \(\times\) & 0.9947 & 0.3384 & 0.0049 & 0.9898 & 67.89 \\  & ✓ & \(\times\) & 0.9961 & 0.3325 & 0.0022 & 0.9939 & 147.81 \\  & ✓ & ✓ & **0.9969** & **0.3765** & **0.0002** & **0.9967** & **1409.55** \\ \hline \hline \end{tabular} Note: The best results are highlighted in bold.  
\end{table} 
Table 5: An ablation study was conducted on three datasets to evaluate the effectiveness of the proposed method. Secondly, this paper examines the impact of the number of temporal embedding nodes \(\gamma_{\text{te}}\) on the results. Generally, increasing the number of temporal embedding nodes improves performance; however, an excessive number of nodes can lead to parameter redundancy, ultimately degrading network performance. Therefore, it is important to set a reasonable number of temporal embedding nodes. As shown in Table 7, all datasets achieve optimal performance with 128 nodes. Accordingly, we set the number of temporal embedding nodes to 128 in our experiments. Thirdly, the regularization parameter \(\delta\) determines the impact of \(\mathcal{L}_{sid}\) on the performance of the network algorithm. An appropriate \(\mathcal{L}_{sid}\) suppresses potential targets, enabling effective background learning and ensuring that the reconstruction results successfully separate the target from the background. However, an excessively large \(\mathcal{L}_{sid}\) can hinder target reconstruction, reducing performance. Table 8 demonstrates that an optimal balance between the two loss functions is achieved at \(\delta=0.01\). Consequently, we set \(\delta\) to 0.01 in our experiments. During the background suppression phase, selecting an appropriate parameter \(\beta\) effectively suppresses the background while highlighting the target. This parameter primarily reflects the strength of the non-linear suppression function in reducing background interference. As shown in Table 9, the background suppression capability increases with a higher beta initially, but then starts to decline. A smaller \(\beta\) leads to insufficient background suppression, while an excessively high beta causes the non-linear function to lose its ability to suppress, failing to preserve potential targets. Based on comprehensive experimental analysis, we selected \(\beta\) values of 8 for Los Angeles and Gulfport, 12 for San Diego.