# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 4 Results
### Comparative Results on HSI Target Detection  
Figure 4 visualizes the detection maps generated by all comparison methods and the proposed method on three real datasets. The results demonstrate that the SID-DN method outperforms the other methods in both target detection and background suppression. Classical detection techniques like MF, CEM and SID exhibit high accuracy in target detection but struggle to filter extensive background information, resulting in insufficient background suppression. Likewise, the DSC method performs poorly in handling complex spectral scenarios, leading to increased false alarm targets. The SID-DN method excels in background suppression, effectively suppressing target-independent background information compared to recently proposed methods such as BLTSC and OS-VAE. BLTSC and OS-VAE, which are generative deep learning models that incorporate background generation representation, slightly lag behind the SID-DN algorithm in background suppression. This confirms the effectiveness of incorporating SID concepts into network design. Furthermore, using SID-DN, particularly on the Los Angeles dataset, the distinction between the target and background is highly evident, demonstrating that the reconstructed background closely resembles the real situation. Additionally, this paper enhances the coarse detection algorithm, significantly reducing target interference in the learning of background samples. The incorporation of SID concepts effectively eliminates potential bad samples, enabling accurate reconstruction of background samples. In summary, the SID-DN method proposed in this paper demonstrates competitive advantages in both target detection and background suppression. Figure 4: Pseudo-color representations of the three datasets are shown together with their corresponding ground-truth images and detection results obtained using various methods. Figure 5 presents the 3D ROC curves corresponding to the above detection results. Compared to the other algorithms, SID-DN achieves results closer to the upper left corner of the test dataset in terms of the AUC\({}_{(\tau,P_{0},\,P_{F})}\) metric. However, SID-DN performs slightly worse than DSE and MF in AUC\({}_{(P_{F},P_{D})}\). This is because SID-DN focuses on learning background features through reconstruction, but the target spectral reconstruction remains suboptimal, resulting in weaker target intensity in detection and impacting this metric. Conversely, SID-DN is closer to the bottom right corner in the AUC\({}_{(\tau,P_{F}(\tau))}\) curve, indicating its superior background suppression capabilities compared to other methods. This observation highlights the importance of considering multiple metrics in evaluating algorithm performance. In summary, SID-DN achieves a highly advantageous average optimal detection rate across all three datasets. While ROC curves visually represent the results, accurately assessing them can be challenging due to overlapping curves among the compared methods. Thus, the five AUC metrics across the three datasets are analyzed in detail in Tables 2-4. In Tables 2-4, the best results are highlighted in bold. The proposed method achieves the best performance on the most critical metric, AUC\({}_{(P_{0},\,P_{F})}\) demonstrating the superiority of the algorithm. AUC\({}_{(\tau,P_{0}(\tau))}\) typically shows an inverse trend with AUC\({}_{(\tau,P_{F}(\tau))}\), where a large value of AUC\({}_{(\tau,P_{0}(\tau))}\) corresponds to a small value of AUC\({}_{(\tau,P_{F}(\tau))}\), or vice versa. Due to limitations in background reconstruction, SID-DN does not achieve the best results