# Spectra-Based Selective Searching for Hyperspectral Anomaly Detection
## 4 Discussion
### Qualitative Analysis  
For the AVIRIS San Diego airport hyperspectral dataset, the detection results of the proposed Triple-S algorithm as well as the methods for comparison are shown in Figure 5a-f. From the detection result image Figure 5a, the detection performance of the GRX algorithm was not satisfactory. The position and shape of the aircraft as the anomaly of the dataset were difficult to identify compared with the ground-truth image as shown in Figure 2b. At the same time, several background pixels were misjudged as anomaly pixels in the red, green and yellow boxes as shown in Figure 5a. In addition, the overall background of the image was not well suppressed. The detection result image of the FFE algorithm Figure 5b looked much better than the result image of the GRX algorithm. The locations and shapes of the three aircraft were been detected but it was still not clear enough. Similar to the GRX algorithm, a number of background pixels in the red, green and yellow boxes were misjudged as anomalies. The overall background of the image was also not well suppressed. In the detection result image of the FEBPAD algorithm, the center position of the three aircraft could be clearly detected, but the edge pixels of the aircraft could not be detected. Meanwhile, a few background pixels in the position of the red region were misjudged as anomalies according to the ground-truth image. But as the name suggests, background purification, this method suppressed background pixels very well among the five algorithms for comparison. In the anomaly detection result of the LSAD-CR-IDW algorithm, not only the locations but also the shapes of three aircraft were detected clearly. Most of the abnormal pixels were correctly detected. However, the disadvantage was that the algorithm detected the spatial distribution of the entire image at the same time. A large number of background pixels had a higher probability of being abnormal pixels, and the false alarm rate was very high. The detection performance of LRSRD was also not satisfactory, because the locations and shapes of the three aircraft were also hardly detected and at the same time many background pixels in the red, green and yellow boxes were misjudged as anomalies. This could be attributed to the inaccurate and incomplete background dictionary. Compared with the other five algorithms, the detection result image of the proposed Triple-S algorithm looked better, in which not only the position and shape of the three aircraft were clearly detected, but also the background pixels were completely suppressed. Owing to the inherent peculiarity of the Triple-S method, it can make an unambiguous distinction between anomalous targets and background. Therefore, the three abnormal aircraft pixels were markedly distinct from the background pixels of the result image, which was similar to the ground-truth map. For the HYDICE urban hyperspectral dataset, the detection results of the proposed Triple-S algorithm as well as the methods for comparison are shown in Figure 6a-f. In the detection result image of the GRX algorithm shown in Figure 6a, most of the locations of anomaly could be detected but not clearly enough compared with the ground-truth image as shown in Figure 3b. Meanwhile, a number of background pixels were misjudged as anomalies in the red and green boxes. Therefore, the suppression of overall background in this image was not satisfactory. From Figure 6b, the detection result of FFE looked better than the result of the GRX algorithm. Most of the abnormal pixels could be clearly detected. However, similar to the GRX algorithm, there were still a large number of background pixels with high anomaly scores in the red box according to the ground-truth image. The overall background of the image was also not well suppressed. Different from the above two algorithms, FEBPAD had a better effect of background suppression.