# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 4 Results
### Comparative Results on HSI Target Detection
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **AUC\({}_{(P_{0},P_{0})}\)** & **AUC\({}_{(\tau,P_{0})}\)** & **AUC\({}_{(\tau,P_{0})}\)** & **AUC\({}_{BS}\)** & **AUC\({}_{SNPR}\)** & **Running Time** \\ \hline
**CEM**[14] & 0.9929 & 0.3618 & 0.0176 & 0.9753 & 20.49 & 0.14 \\
**SID**[18] & 0.8499 & 0.2015 & 0.0361 & 0.8138 & 5.572 & 0.09 \\
**MF**[54] & 0.9958 & 0.4306 & 0.1015 & 0.8943 & 4.24 & **0.08** \\
**DSE**[32] & 0.9470 & **0.4725** & 0.0762 & 0.8708 & 6.19 & 21.92 \\
**BLTSC**[41] & 0.9944 & 0.3353 & 0.0011 & 0.9933 & 297.95 & 2.83 \\
**OS-VAE**[45] & 0.9943 & 0.3311 & 0.0010 & 0.9933 & 318.38 & 2.69 \\
**Proposed** & **0.9967** & 0.3146 & **0.0007** & **0.9960** & **410.53** & 0.37 \\ \hline \hline \end{tabular} Note: The best results are highlighted in bold.  
\end{table} 
Table 2: Performance comparison of different methods across various AUC metrics on the Los Angeles dataset. Figure 5: ROC curves of different methods across three datasets: (**a**) Los Angeles, (**b**) Gulport, and (**c**) San Diego. From left to right: 3-D ROC curve, 2-D ROC curve for \((P_{F},P_{D})\), 2-D ROC curve for \((\tau,P_{D}(\tau))\), and 2-D ROC curve for \((\tau,P_{F}(\tau))\). in AUC\({}_{(\tau,P_{D}(\tau))}\). However, it achieves the highest average performance in AUC\({}_{(\tau,P_{F}(\tau))}\), AUC\({}_{BS}\) and AUC\({}_{SNPR}\), strongly indicating its effectiveness in suppressing the complex backgrounds of HSI. In conclusion, the AUC scores from the comprehensive performance analysis highlight the sophistication and robustness of the proposed SID-DN algorithm. This paper further evaluates the target-background separation performance of SID-DN compared to other methods across three datasets. As shown in Figure 6, SID-DN achieves superior target-to-background separation ratios, better separation between target and background, and smaller overlapping regions across all datasets. Additionally, SID-DN effectively suppresses background interference, further demonstrating the algorithm's effectiveness. In conclusion, the proposed SID-DN method delivers outstanding performance in both subjective and objective metrics, including detection maps and 3D-ROC curves, while demonstrating superior target-background separation capability across multiple datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **AUC\({}_{(P_{r},P_{D})}\)** & **AUC\({}_{(\tau,P_{D})}\)** & **AUC\({}_{(\tau,P_{F})}\)** & **AUC\({}_{BS}\)** & **AUC\({}_{SNPR}\)** & **Running Time** \\ \hline
**CEM**[14] & 0.9955 & 0.6265 & 0.0395 & 0.9560 & 15.82 & **0.007** \\
**SID**[18] & 0.9916 & 0.1588 & 0.0035 & 0.9881 & 44.27 & 0.083 \\
**MF**[54] & 0.9962 & **0.7093** & 0.2140 & 0.7822 & 3.30 & 0.024 \\
**DSE**[32] & 0.9941 & 0.6447 & 0.0455 & 0.9486 & 14.152 & 71.08 \\
**BLTSC**[41] & 0.9961 & 0.4167 & 0.0019 & 0.9942 & 215.57 & 2.54 \\
**OS-VAE**[45] & 0.9951 & 0.3454 & 0.0013 & 0.9938 & 253.7 & 2.7 \\
**Proposed** & **0.9969** & 0.3765 & **0.0002** & **0.9967** & **1409.55** & 0.35 \\ \hline \hline \end{tabular} Note: The best results are highlighted in bold.  
\end{table} 
Table 4: Performance comparison of different methods across various AUC metrics on San Diego dataset. Figure 6: Background-target separation plots for various methods across three datasets: (**a**) Los Angeles, (**b**) Gulport, and (**c**) San Diego.