# Spectra-Based Selective Searching for Hyperspectral Anomaly Detection
## 3 Results  
Verification experiments on two real typical hyperspectral datasets were conducted in this section to demonstrate the anomaly detection performance of the proposed spectra-based selective searching (Triple-S) algorithm. Five previous excellent anomaly detection methods, such as GRX [19], FEE [29], FEBPAD [30], LSAD-CR-IDW [40] and LRSRD [38] were used to verify the effectiveness of the Triple-S algorithm proposed in this paper. The validity of the proposed Triple-S method and these representative methods for comparison on these two real hyperspectral datasets are displayed, including detection results, receiver operating characteristic (ROC) curve [47], the area under the ROC curve (AUC) values [48] and the running time. All the algorithms were implemented in MATLAB on a Dell XPS13 personal laptop with an Intel Core i5-8250U CPU with 8 GB of RAM.  
To assess our Triple-S anomaly detection algorithm both qualitatively and quantitatively, an experiment was conducted to evaluate the anomaly detection performance of the proposed Triple-S algorithm. Meanwhile, five typical anomaly detection algorithms, GRX [19], FEE [29], FEBPAD [30], LSAD-CR-IDW [40] and LRSRD [38], were employed for comparison and the corresponding detection results were obtained from the source code shared on GitHub.  
As described in Section 2.2, the proposed Triple-S anomaly detector consists of two steps, oversegmentation and region merging. The results of our proposed Triple-S algorithm are shown in Figure 4a,b for AVIRIS San Diego airport dataset and Figure 4c,d for HYDICE Urban dataset. Specifically, Figure 4a,c are separately the results of oversegmentation for two test hyperspectral images. Figure 4b,d are separately the results of region merging for two test hyperspectral images. The implementation of these five algorithms used for comparison are according to the source codes from GitHub and the principles of the respective algorithms have been briefly introduced in the Introduction section and were described in detail in the corresponding references. The detection results of five comparable algorithms as well as the proposed Triple-S algorithm are shown in Figures 5a-f and 6a-f for these two test hyperspectral images. In order to assess the detection accuracy, the ROC curves of different anomaly detectors for the quantitative comparison of detection performance are shown in Figure 7 for AVIRIS San Diego airport dataset and Figure 8 for HYDICE Urban dataset.  
Figure 3: Second real-world data set: HYDICE urban scene. (**a**) False color image; (**b**) ground-truth map.  
Figure 4: Results of oversegmentation and region merging of (**a,b**) AVIRIS San Diego airport dataset (**c,d**) HYDICE urban dataset. (**a**) The result of oversegmentation for the San Diego dataset, (**b**) the result of region merging for the San Diego dataset, (**c**) the result of oversegmentation for the Urban dataset, (**d**) the result of region merging for the Urban dataset.  
Figure 5: Detection results of (**a**) GRX, (**b**) FEE, (**c**) FEBPAD, (**d**) ISAD-CR-IDW, (**e**) LRSRD, and (**f**) Triple-S for AVIRIS San Diego airport dataset.  
Figure 6: Detection results of (**a**) GRX, (**b**) FEE, (**c**) FEBPAD, (**d**) LSAD-CR-IDW, (**e**) LRSRD, and (**f**) Triple-S for HYDICE Urban data set.  
Figure 7: Receiver operating characteristic (ROC) curves for AVIRIS San Diego airport dataset.  
The corresponding AUC values are summarized in Table 1. As an important aspect to evaluate the algorithm efficiency, the running times of each algorithm are listed in Table 2. Based on these detection results and the performance of the algorithms for comparison, several related assessments were carried out both qualitatively and quantitatively to evaluate the effectiveness of our Triple-S anomaly detection algorithm, which are described in the discussion section in detail.