# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 6 Conclusions  
This paper studies the challenges in generative HTD. From a theoretical perspective, we analyze the key aspects of generative HTD and address the shortcomings of the original algorithm. From a technical perspective, we propose a novel deep learning model that integrates SID with Diffusion to effectively guide the reconstruction of the generative model, incorporating SID information constraints. The SID loss and Diffusion process are co-optimized to enhance the reconstruction capability of the generative model. Experiments on multiple real HSI datasets demonstrate the effectiveness and robustness of the SID-DN algorithm. The results indicate that the SID-DN algorithm achieves state-of-the-art performance in both background suppression and detection accuracy when compared to existing methods. Specifically, our method provides an improvement of at least 50% in \(AUC_{SNP}\).Future work will focus on optimizing the coarse detection process to minimize the influence of hyperparameters and further enhance detection accuracy, improving overall network performance.Furthermore, we plan to explore the potential applications of our proposed algorithm in real-world scenarios, such as military detection and environmental monitoring, as well as attempt multi-modal data fusion with hyperspectral and LiDAR data. Additionally, we aim to investigate target detection in complex scenes using SID-DN in combination with UAV-based hyperspectral imaging systems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & & **U-Net** & **VIT** & **SID-DN** \\ \hline \multirow{3}{*}{Los Angeles} & Params & 1.15 M & 0.73 M & **0.46 M** \\  & FLOPs & 1.1 G & 0.75 G & **0.47 G** \\  & \(AUC_{(P_{D},P_{F})}\) & 0.9960 & 0.9961 & **0.9965** \\ \hline \multirow{3}{*}{Gulport} & Params & 1.13 M & 0.71 M & **0.44 M** \\  & FLOPs & 1.1G & 0.73 G & **0.45 G** \\  & \(AUC_{(P_{D},P_{F})}\) & 0.9992 & 0.9992 & **0.9994** \\ \hline \multirow{3}{*}{Sandiego} & Params & 1.13 M & 0.83 M & **0.43 M** \\  & FLOPs & 1.1 G & 0.85 G & **0.44 G** \\ \cline{1-1}  & \(AUC_{(P_{D},P_{F})}\) & 0.9958 & 0.9954 & **0.9969** \\ \hline \hline \end{tabular} Note: The best results are highlighted in bold.  
\end{table}
Table 10: The performance comparison of different denoising diffusion models three datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **4** & **8** & **12** & **16** & **20** \\ \hline Los Angeles & 0.9964 & **0.9967** & 0.9962 & 0.9953 & 0.9938 \\ Gulport & 0.9993 & **0.9994** & 0.9993 & 0.9993 & 0.9992 \\ San Deigo & 0.842 & 0.9822 & 0.9907 & 0.9901 & **0.9969** \\ \hline \hline \end{tabular} Note: The best results are highlighted in bold.  
\end{table}
Table 9: Sensitivity analysis of \(\beta\) was conducted on three datasets.  
**Author Contributions:** Conceptualization, [PERSON]; data curation, X.D. and F.L.; formal analysis, [PERSON]; methodology, J.G.; software, J.G. and Z.H.; validation, Z.H. and Z.Y.; writing--original draft, J.G.; writing--review and editing, Z.H., X.D., Z.Y. and F.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.